{"repo": "123fjh/circuitmind", "files": {"GUI/utils/config.py": {"4677b755a718f5b15fc304ba3866a8e04a9a2902": {"t": 1785595484.7758398, "n": 20898, "h": "179d0fcb46040503", "c": "# utils/config.py\nimport os\n# 配置字典\napi_configs = {\n    \"glm-4-plus\": {\n        \"API_KEY\": [\"780fce9dabb5a1005e38baf6b6e556d8.VCLi2kOdjLs6DQtM\"],\n        \"BASE_URL\": \"https://open.bigmodel.cn/api/paas/v4/\",\n    },\n    \"deepseek-chat\": {\n        \"API_KEY\": [\"sk-f73a42bc3f1643b69059757101005d0d\"],\n        \"BASE_URL\": \"https://api.deepseek.com\",\n    },\n    \"gpt-4o\": {\n        \"API_KEY\": [\"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\"],\n        \"BASE_URL\": \"https://api.openai.com/v1\",\n    },\n    \"gpt-4o-mini\": {\n        \"API_KEY\": [\"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\"],\n        \"BASE_URL\": \"https://api.openai.com/v1\",\n    },\n    \"glm-4-air\":{\n        \"API_KEY\": [\"780fce9dabb5a1005e38baf6b6e556d8.VCLi2kOdjLs6DQtM\"],\n        \"BASE_URL\": \"https://open.bigmodel.cn/api/paas/v4/\",\n    },\n    \"qwq:latest\":{\n        \"API_KEY\": [\"ollama\"],\n        \"BASE_URL\": \"http://10.130.148.206:11434/v1\",\n        },\n    \"qwq:32b\":{\n        \"API_KEY\": [\"ollama\"],\n        \"BASE_URL\": \"http://10.130.149.18:11434/v1\",\n        },\n    \n    \"qwq:32b-q8_0\":{\n        \"API_KEY\": [\"ollama\"],\n        \"BASE_URL\": \"http://10.130.144.196:11434/v1\",\n    },\n    \"qwen2.5-coder:14b\":{\n        \"API_KEY\": [\"ollama\"],\n        \"BASE_URL\": \"http://10.130.149.18:11434/v1\",\n    },\n    \"qwen2.5:14b\":{\n        \"API_KEY\": [\"ollama\"],\n        \"BASE_URL\": \"http://10.130.144.196:11434/v1\",\n    },\n    \"deepseek-coder:33b\":{\n        \"API_KEY\": [\"ollama\"],\n        \"BASE_URL\": \"http://10.130.148.206:11434/v1\", \n    },\n    \"deepseek-coder:6.7b\":{\n        \"API_KEY\": [\"ollama\"],\n        \"BASE_URL\": \"http://10.130.149.18:11436/v1\", \n    },\n    \"qwen2.5:7b\":{\n        \"API_KEY\": [\"ollama\"],\n        \"BASE_URL\": \"http://localhost:11434/v1\",\n    },\n    \"qwen2.5:7b-instruct-fp16\":{\n        \"API_KEY\": [\"ollama\"],\n        \"BASE_URL\": \"http://10.130.148.206:11434/v1\",\n    },\n    \"qwen2.5-coder:32b\":{\n        \"API_KEY\": [\"ollama\"],\n        \"BASE_URL\": \"http://10.130.148.206:11436/v1\",\n    },\n    \"deepseek-ai/DeepSeek-V3\":{\n        \"API_KEY\": [\"sk-ptyrqcoakodsijdqglxbzucezfvdmdolzxxfafdpozixljdr\"],\n        \"BASE_URL\":\"https://api.siliconflow.cn/v1\"\n    },\n    \"deepseek-r1-250120\":{\n        \"API_KEY\": [\"009039c2-755b-4abd-8d76-225b3456ea29\",\"0a12f045-e34a-46b2-b3a8-999ff9342fcc\",\"137d25a5-b809-4a3c-a2ec-22d984d653df\",\"4ac99dcf-cf55-4e56-b25c-879d80e59e2c\",],\n        \"BASE_URL\":\"https://ark.cn-beijing.volces.com/api/v3/\"\n    },\n    \"deepseek-r1\":{\n        \"API_KEY\": [\"sk-da2iftmgizerrfjj\"],\n        \"BASE_URL\":\"https://cloud.infini-ai.com/maas/v1\"\n    },\n    \"deepseek-r1-distill-qwen-32b\":{\n        \"API_KEY\": [\"sk-da2jurhjtunkwnuz\"],\n        \"BASE_URL\":\"https://cloud.infini-ai.com/maas/v1\"\n    },\n    \"qwq-32b\":{\n        \"API_KEY\": [\"sk-da2juiy2cochoavl\"],\n        \"BASE_URL\":\"https://cloud.infini-ai.com/maas/v1\"\n    },\n    \"deepseek-v3\":{\n        \"API_KEY\": [\"sk-da2iftmgizerrfjj\"],\n        \"BASE_URL\":\"https://cloud.infini-ai.com/maas/v1\"\n    },\n    \"qwen2.5-coder-32b-instruct\":{\n        \"API_KEY\": [\"sk-da2jurhjtunkwnuz\"],\n        \"BASE_URL\":\"https://cloud.infini-ai.com/maas/v1\"\n    },\n    \"deepseek-v3-250324\":{\n        \"API_KEY\": [\"e99646c6-b52a-45a2-ad49-c17415174d8d\"],\n        \"BASE_URL\":\"https://ark.cn-beijing.volces.com/api/v3/\"\n    },\n    \"deepseek-r1:70b\":{\n        \"API_KEY\": [\"ollama\"],\n        \"BASE_URL\": \"http://10.130.144.196:11434/v1\",\n    },\n    \"phi4:latest\":{\n        \"API_KEY\": [\"ollama\"],\n        \"BASE_URL\": \"http://10.130.144.196:11434/v1\",\n    },\n    \"deepseek-r1:32b\":{\n        \"API_KEY\": [\"ollama\"],\n        \"BASE_URL\": \"http://10.130.144.208:11437/v1\",\n    },\n    \"deepseek-r1:14b\":{\n        \"API_KEY\": [\"ollama\"],\n        \"BASE_URL\": \"http://10.130.144.208:11436/v1\",\n    },\n    \n        \n    \"gemini-2.0-flash\":{\n        \"API_KEY\": [\"AIzaSyB0PscblS6t4TSreKYY3uFPMHJb1nwA4b0\",\"AIzaSyAWmeLGAx9RctoqbLlaHt__g747P3Bdg3s\",],\n         \"BASE_URL\": \"https://generativelanguage.googleapis.com/v1beta/\"\n    },\n    \"gemini-2.0-flash-thinking-exp\":{\n        \"API_KEY\": [\n                \"AIzaSyCe1FCm51LdQGv_lQalEeYwz7OsEfwauwg\",\n                \"AIzaSyCsdInwWPMIFMJ3vQJfiTKRZPnsRmLZgJY\",\n                \"AIzaSyCVpmMEKNuRJRqVzCWmVfUKfFnbKZitYxI\",\n                \"AIzaSyDp_C9d7fxlPw2n71vokC7zbV1AIOsp-zM\",\n                \"AIzaSyCATsoE4Luq3AtoZIUbzyPZVFHPeWEn1QU\",\n                \"AIzaSyBO13wFHgRgDtdw_vKtX853q_zMdmTBPV8\",\n                \"AIzaSyBhdQ6MRWk5wkxFKOrABAXDFaBGVWIzOXs\",\n                \"AIzaSyAWmeLGAx9RctoqbLlaHt__g747P3Bdg3s\",\n                \"AIzaSyD5xok4XZST4m4kaxRZPxtyrBFaFgTN7jM\",\n                \"AIzaSyCYfvOUl8sCo8spAtq4Oe20ZFIiCJyHtY8\",\n                \"AIzaSyDqQNRPlppxGW_m6bSS6lcPdAZzWZpbEGQ\",\n                \"AIzaSyCRMMkCtRnAFj5E3X5H1lnIq5HeJXKhqpw\",\n                \"AIzaSyCvs-dZ-oO-T3NAYMAC2PzEbmUz824WJds\",\n                \"AIzaSyB2NYBW-cQ8KPE_ksMO5qZRFHRJ8GOXpoE\",\n                \"AIzaSyCogr95x1PyNeHNuG-WK7Bkw9piSyRbMEY\",\n                \"AIzaSyCxxCys9WVW3FuPgRWzomXOhGEqgPTenDA\",\n                ],\n        \"BASE_URL\": \"https://generativelanguage.googleapis.com/v1beta/\"\n    },\n    \n    \"gemini-2.0-pro-exp\":{\n        \"API_KEY\": [\n                \"AIzaSyCe1FCm51LdQGv_lQalEeYwz7OsEfwauwg\",\n                \"AIzaSyCxxCys9WVW3FuPgRWzomXOhGEqgPTenDA\",\n                \"AIzaSyCsdInwWPMIFMJ3vQJfiTKRZPnsRmLZgJY\",\n                \"AIzaSyCVpmMEKNuRJRqVzCWmVfUKfFnbKZitYxI\",\n                \"AIzaSyDp_C9d7fxlPw2n71vokC7zbV1AIOsp-zM\",\n                \"AIzaSyCATsoE4Luq3AtoZIUbzyPZVFHPeWEn1QU\",\n                \"AIzaSyBO13wFHgRgDtdw_vKtX853q_zMdmTBPV8\",\n                \"AIzaSyBhdQ6MRWk5wkxFKOrABAXDFaBGVWIzOXs\",\n                \"AIzaSyAWmeLGAx9RctoqbLlaHt__g747P3Bdg3s\",\n                \"AIzaSyD5xok4XZST4m4kaxRZPxtyrBFaFgTN7jM\",\n                \"AIzaSyCYfvOUl8sCo8spAtq4Oe20ZFIiCJyHtY8\",\n                \"AIzaSyDqQNRPlppxGW_m6bSS6lcPdAZzWZpbEGQ\",\n                \"AIzaSyCRMMkCtRnAFj5E3X5H1lnIq5HeJXKhqpw\",\n                \"AIzaSyCvs-dZ-oO-T3NAYMAC2PzEbmUz824WJds\",\n                \"AIzaSyB2NYBW-cQ8KPE_ksMO5qZRFHRJ8GOXpoE\",\n                \"AIzaSyCogr95x1PyNeHNuG-WK7Bkw9piSyRbMEY\",\n                ],\n        \"BASE_URL\": \"https://generativelanguage.googleapis.com/v1beta/\"\n    },\n    \"gemini-2.0-flash-exp\":{\n      \n        \"API_KEY\": [\n                \"AIzaSyCsdInwWPMIFMJ3vQJfiTKRZPnsRmLZgJY\",\n                \"AIzaSyCVpmMEKNuRJRqVzCWmVfUKfFnbKZitYxI\",\n                \"AIzaSyDp_C9d7fxlPw2n71vokC7zbV1AIOsp-zM\",\n                \"AIzaSyCATsoE4Luq3AtoZIUbzyPZVFHPeWEn1QU\",\n                \"AIzaSyBO13wFHgRgDtdw_vKtX853q_zMdmTBPV8\",\n                \"AIzaSyBhdQ6MRWk5wkxFKOrABAXDFaBGVWIzOXs\",\n                \"AIzaSyAWmeLGAx9RctoqbLlaHt__g747P3Bdg3s\",\n                \"AIzaSyD5xok4XZST4m4kaxRZPxtyrBFaFgTN7jM\",\n                \"AIzaSyCYfvOUl8sCo8spAtq4Oe20ZFIiCJyHtY8\",\n                \"AIzaSyDqQNRPlppxGW_m6bSS6lcPdAZzWZpbEGQ\",\n                \"AIzaSyCRMMkCtRnAFj5E3X5H1lnIq5HeJXKhqpw\",\n                \"AIzaSyCvs-dZ-oO-T3NAYMAC2PzEbmUz824WJds\",\n                \"AIzaSyB2NYBW-cQ8KPE_ksMO5qZRFHRJ8GOXpoE\",\n                \"AIzaSyCogr95x1PyNeHNuG-WK7Bkw9piSyRbMEY\",\n                \"AIzaSyCe1FCm51LdQGv_lQalEeYwz7OsEfwauwg\",\n                \"AIzaSyCxxCys9WVW3FuPgRWzomXOhGEqgPTenDA\",\n                ],\n            \n        \"BASE_URL\": \"https://generativelanguage.googleapis.com/v1beta/\"\n    },\n    \"grok-3-latest\": {\n        \"API_KEY\": [\"xai-PRXLUHFvDdVPqNunuWOp2aGrG6h520hR3xXq5WiriowRlONwtbkC6bN3VcCDRw2ROBj8M23IwaQBMCiS\"],\n        \"BASE_URL\": \"https://api.x.ai/v1\"\n    },\n    \"RV-Qwen3-S1\":{\n        \"API_KEY\": [\"ollama\"],\n        \"BASE_URL\": \"http://10.130.144.196:11434/v1\",\n    }\n\n}\n\n\n# 获取指定模型的配置 (返回 API 密钥列表和 base URL)\ndef get_api_keys_base_url(model_name):\n    config = api_configs.get(model_name, None)\n    if config:\n        return config['API_KEY'], config['BASE_URL']\n    return None, None\n\n# 切换 API key (轮询)\ndef switch_api_key(model_name):\n    config = api_configs.get(model_name, None)\n    if config and 'API_KEY' in config and len(config['API_KEY']) > 1: # 确保有多个 API keys 可以切换\n        config['current_key_index'] = (config['current_key_index'] + 1) % len(config['API_KEY']) # 轮询切换索引\n        return config['API_KEY'][config['current_key_index']]\n    elif config and 'API_KEY' in config and len(config['API_KEY']) == 1:\n        return config['API_KEY'][0] # 只有一个 key, 返回这一个\n    return None # 没有找到配置或 API keys\n\ndef get_api_config(model_name):\n    return api_configs.get(model_name, None)\n\n\n\nfor model, config in api_configs.items():\n    if 'API_KEY' in config:\n        config['current_key_index'] = 0\n\ndef set_key_index(model_name, key_index):\n    config = api_configs.get(model_name, None)\n    if config and 'API_KEY' in config:\n        if 0 <= key_index < len(config['API_KEY']):\n            config['current_key_index'] = key_index\n        else:\n            print(f\"Warning: key_index {key_index} is out of range for model {model_name}. Using the first key.\")\n            config['current_key_index'] = 0\n\ndef get_current_api_key(model_name):\n    config = api_configs.get(model_name, None)\n    if config and 'API_KEY' in config:\n        key_index = config.get('current_key_index', 0)\n        if 0 <= key_index < len(config['API_KEY']):\n            return config['API_KEY'][key_index]\n        else:\n            return config['API_KEY'][0]\n    return None\n\ndef update_config(model_name, key_index):\n    global MODEL_NAME, OPENAI_API_KEY, OPENAI_BASE_URL\n    MODEL_NAME = model_name\n    set_key_index(model_name, key_index)\n    API_KEY, BASE_URL = get_api_keys_base_url(model_name)\n    OPENAI_API_KEY = get_current_api_key(model_name)\n    OPENAI_BASE_URL = BASE_URL\ndef get_experiment_output_root():\n    return f\"./experiments_output-v2-merged/{MODEL_NAME}/\"\n\n# 模型配置\n# MODEL_NAME = \"deepseek-ai/DeepSeek-V3\"  # 指定模型名称\n# MODEL_NAME = \"deepseek-r1-250120\"  # 指定模型名称\n# MODEL_NAME = \"gemini-2.0-pro-exp\"\n# MODEL_NAME = \"gemini-2.0-flash-thinking-exp\"\n# MODEL_NAME = \"qwen2.5:7b\"\n\n\n\n\nKNOWLEDGE_BASE_PATH = \"./knowledge_base/RAG-data\"  # 知识库路径\nDETAIL_KNOWLEDGE_BASE_PATH = \"./knowledge_base/RAG-data-detail\"  # 知识库路径\nRAG_INDEX_PATH = \"./knowledge_base/RAG-data/vector_index.faiss\"  # 索引路径\nRAG_DATA_PATH = \"./knowledge_base/RAG-data/vector_data.json\"  # 数据路径\nEMBEDDING_MODEL = \"nomic-embed-text\"  # 嵌入模型\nRAG_LLM_MODEL = \"qwen2.5-coder:14b\"  # LLM 模型\n# RAG_LLM_MODEL = \"grok-3-latest\"\nRAG_ONE = True\nRAG_TWO = True\n# 其他配置\n# EXPERIMENTS_ROOT = \"./TC/Easy\"  #  定义实验根目录 \n# EXPERIMENTS_ROOT = \"./TC/Medium\"\nEXPERIMENTS_ROOT = \"./TC/Datasets-TC\"\nTESTBENCH_PATH = \"\"\nBASIC_GATES_ONLY = True\n\n# VERILOG_DIR 将在 main.py 中动态设置\n# REFERENCE_CODE_PATH 将在 main.py 中动态设置\nMAX_RETRY_ATTEMPTS = 2\nMAX_AUTO_FIX_ATTEMPTS = 2 # 最大自动修复次数\n\n# SUMMARY_FILE 将在 main.py 中动态设置\nCOMMON_ERRORS_FILE = \"./knowledge_base/common_errors.md\"  # 新增：常见错误记录文件\n\n# 新增：指定所有实验结果的存储根目录\n# EXPERIMENT_OUTPUT_ROOT = f\"./experiments_output/{MODEL_NAME}/\"\n# EXPERIMENT_OUTPUT_ROOT = f\"./experiments_output/qwen2.5-coder:14b/\"\n\nAGENT_SYSTEM_MESSAGES = {\n     \"CoderAgent\": \"\"\"You are a highly skilled Verilog code generation assistant, focused on implementing circuit functionality using **structural Verilog**.\n\n**Key Constraints and Guidelines:**\n\n1. **No Behavioral Descriptions:** You **MUST NOT** use behavioral level constructs such as `always`, `if`, `case`, `for` loops (except for generate statements), or any other procedural blocks within the generated code. Focus exclusively on structural implementations using basic logic gates and module instantiations.\n2. **Basic Logic Gates EXCLUSIVELY (if applicable):** If the task requires implementation using basic logic gates, use only `and`, `or`, `not`, `xor`, `xnor`, `nand`, `nor`.\n3. **Utilize Provided Modules:** If specific module implementations are provided, you **MUST** instantiate and use those modules in your design. Do not attempt to reimplement their functionality using basic gates or behavioral descriptions.\n4. **Structural Verilog is Mandatory:** Construct your Verilog code using a structural approach, primarily by instantiating basic logic gates and the pre-defined modules provided.\n5. **Output Generation:** Generate outputs by connecting the outputs of instantiated modules or through combinations of basic logic gate assignments.\n6. **`reg` Output Handling:** If an output is declared as `reg`, ensure it is driven by the output of an instantiated module or basic gate logic, not by an `always` block. Consider using `output wire` where appropriate.\n7. **Synchronous Design for Sequential Logic:** Implement sequential logic by instantiating provided flip-flop modules, ensuring adherence to synchronous design principles.\n8. **No Delay Statements:** Delay statements (like `#1`, `#100`) are strictly forbidden.\n\nThink step by step about the circuit's structure, focusing on how to connect basic gates and provided modules to achieve the desired functionality. Remember, **no `always` blocks or `if` statements are allowed in the generated code.**\n\"\"\",\n     \"Reviewer\": \"\"\"You are an expert Verilog code review assistant, specializing in ensuring code correctness and strict adherence to implementation constraints, **with a primary focus on enforcing purely structural Verilog code.**\n\n**Review Focus:**\n1. **ABSOLUTE PROHIBITION OF BEHAVIORAL DESCRIPTIONS:** **Immediately and unequivocally flag any use of behavioral level constructs.** This includes `always` blocks (except for the allowed usage shown below), `if` statements, `case` statements, procedural `for` loops, `fork`, `join`, and any other procedural blocks. The reviewed code **must be purely structural and synthesizable**.\n\n    *   **Allowed `always` block usage:** `always @(posedge clk)` blocks are ONLY permitted for assigning values to `output reg` signals, and ONLY when these outputs are directly driven by the `q` output of a D flip-flop. The assignments within these `always` blocks MUST be non-blocking (`<=`). No other logic is allowed inside these `always` blocks. Example:\n        ```verilog\n        always @(posedge clk) begin\n            data_out <= delayed_out;\n        end\n        ```\n2. **Correct Usage of Basic Gates:** Verify that the code **exclusively** uses the allowed basic logic gates (`and`, `or`, `not`, `xor`, `xnor`, `nand`, `nor`) for combinational logic. The code should directly instantiate these gates using their standard Verilog names (e.g., `and a1(out, in1, in2);`). **Under no circumstances should these basic gates be redefined or reimplemented.**\n\n3. **Mandatory Use of Provided Modules:** **Critically check** if the `CoderAgent` has correctly instantiated and used any module implementations provided by the `RetrievalAgent`. If a module (like `d_flip_flop`) was provided, its instantiation is **mandatory**.\n\n4. **Correct Output Generation:** Ensure outputs are generated ONLY by:\n    *   Connecting directly to the output of an instantiated module (like the `q` output of a `d_flip_flop`).\n    *   Connecting directly to the output of a basic logic gate.\n    *   Using `assign` statements for direct wire connections, NOT for implementing combinational logic.\n    *   `output reg` signals must ONLY be driven by `always @(posedge clk)` blocks as described in point #1 or by the output of a predefined module that outputs a `reg` signal.\n\n5. **No Delay Statements:** Confirm the absence of any delay statements (e.g., `#1`, `#10`).\n\n6. **Synchronous Sequential Logic Implementation:** If sequential logic is required, verify that it is implemented **solely** by instantiating the provided `d_flip_flop` modules and follows synchronous design principles.\n\n7. **No `generate` keyword**: `generate` keyword is allowed in Verilog, do not report error if it is used.\n8. **No `module` or `endmodule` outside of module declarations**: These keywords must only be used to declare the start and end of a module definition.\n\n**Feedback Guidelines:**\n\n*   Provide **precise, forceful, and unambiguous feedback** regarding any use of behavioral constructs or incorrect usage of basic gates. Clearly explain that behavioral constructs are **strictly prohibited** and must be replaced with structural equivalents using **explicit gate-level instantiations** or **module instantiations**.\n*   **Explicitly reject any code that redefines or reimplements basic logic gates.** These are Verilog primitives and must be used directly.\n*   Suggest **specific, actionable corrections**, and when possible, provide **concise code snippets** demonstrating the correct structural approach.\n*   **Prioritize feedback** on violations related to:\n    *   The **absolute prohibition of behavioral descriptions.**\n    *   The correct usage of **built-in basic gates** without redefinition.\n    *   The **mandatory instantiation** of provided modules.\n*   **Point out specific line numbers and code snippets** where errors occur.\n*   **Use the provided error codes** to categorize issues.\n\nYour primary goal is to ensure the `CoderAgent` produces a correct implementation that is **100% structural Verilog**. **Any deviation from structural Verilog is unacceptable and must be flagged.**\n\"\"\",\n    \"Executor\": \"You are a Verilog code execution assistant, responsible for compiling and simulating Verilog code.\",\n    \"Summarizer\": \"You are an experiment summarization assistant, responsible for analyzing the experimental process and summarizing findings.\"\n}\n\n\n\n\n# 新增：通过 config 文件指定要测试的文件夹，可以是一个列表\n# 可以指定要测试的特定文件夹的路径，留空则测试 EXPERIMENTS_ROOT 下的所有子文件夹\n\n# TARGET_EXPERIMENTS = [\"./TC/Hard/12_odd_change\",\"./TC/Hard/19_elegant_storage\",\"./TC/Hard/20_store_byte\",\"./TC/Hard/24_box\",\"./TC/Hard/25_counter\",\"./TC/Medium/8_odd_signal\",\"./TC/Hard/28_conditional_checker\",\n#                        \"./TC/Easy/14_or_8bit\",\"./TC/Easy/15_not_8bit\",\"./TC/Easy/21_decoder_1bit\",\n#                       \"./TC/Medium/7_double_trouble\",\"./TC/Medium/8_odd_signal\",\n#                       \"./TC/Medium/9_counting_signals\",\"./TC/Medium/10_half_adder\",\"./TC/Medium/11_full_adder\",\"./TC/Medium/16_adder_8bit\",\n#                       \"./TC/Medium/17_mux_8bit\",\"./TC/Medium/18_opposite_number\",\"./TC/Medium/22_decoder_3bit\",\"./TC/Medium/27_instruction_decoder\",\n#                       ]\n# TARGET_EXPERIMENTS = [\"./TC/Easy/1_not_gate\",\"./TC/Easy/2_second_tick\",\"./TC/Easy/3_xor_gate\",\n#                       \"./TC/Easy/4_or3_gate\",\"./TC/Easy/5_and3_gate\",\"./TC/Easy/6_xnor_gate\",\"./TC/Medium/7_double_trouble\",\"./TC/Medium/8_odd_signal\",\"./TC/Medium/9_counting_signals\",\"./TC/Medium/10_half_adder\",\"./TC/Medium/11_full_adder\",\n#                       \"./TC/Hard/12_odd_change\",\"./TC/Easy/13_inverter_1bit\",\"./TC/Easy/14_or_8bit\",\"./TC/Easy/15_not_8bit\",\"./TC/Medium/16_adder_8bit\",\"./TC/Medium/17_mux_8bit\",\"./TC/Medium/18_opposite_number\",\n#                       \"./TC/Hard/19_elegant_storage\",\"./TC/Hard/20_store_byte\",\"./TC/Easy/21_decoder_1bit\",\n#                       \"./TC/Medium/22_decoder_3bit\",\"./TC/Hard/24_box\",\"./TC/Hard/25_counter\",\"./TC/Hard/26_arithmetic_engine\",\"./TC/Medium/27_instruction_decoder\",\"./TC/Hard/28_conditional_checker\"]  \n                  \n# TARGET_EXPERIMENTS = [\"./TC/Easy/1_not_gate\",\"./TC/Easy/2_second_tick\",]\n# TARGET_EXPERIMENTS = [\"./TC/Hard/25_counter\",\"./TC/Hard/26_arithmetic_engine\",\n#                       ]\n# Hard\n# TARGET_EXPERIMENTS = [\"./TC/Hard/12_odd_change\"]\n# TARGET_EXPERIMENTS = [\"./TC/Hard/19_elegant_storage\"]\n# TARGET_EXPERIMENTS = [\"./TC/Hard/20_store_byte\"]  \n# TARGET_EXPERIMENTS = [\"./TC/Hard/23_logic_engine\"] \n# TARGET_EXPERIMENTS = [\"./TC/Hard/24_box\"]\n# TARGET_EXPERIMENTS = [\"./TC/Hard/25_counter\"]\n# TARGET_EXPERIMENTS = [\"./TC/Hard/26_arithmetic_engine\"] \n# TARGET_EXPERIMENTS = [\"./TC/Hard/28_conditional_checker\"]\n# Easy\n# TARGET_EXPERIMENTS = [\"./TC/Easy/1_not_gate\"]\nTARGET_EXPERIMENTS = [\"./TC/Easy/2_second_tick\"]\n# TARGET_EXPERIMENTS = [\"./TC/Easy/3_xor_gate\"]\n# TARGET_EXPERIMENTS = [\"./TC/Easy/4_or3_gate\"]\n# TARGET_EXPERIMENTS = [\"./TC/Easy/5_and3_gate\"]\n# TARGET_EXPERIMENTS = [\"./TC/Easy/6_xnor_gate\"]\n# TARGET_EXPERIMENTS = [\"./TC/Easy/13_inverter_1bit\"]\n# TARGET_EXPERIMENTS = [\"./TC/Easy/14_or_8bit\"] \n# TARGET_EXPERIMENTS = [\"./TC/Easy/15_not_8bit\"]\n# TARGET_EXPERIMENTS = [\"./TC/Easy/21_decoder_1bit\"]\n\n#Meidum \n# TARGET_EXPERIMENTS = [\"./TC/Medium/7_double_trouble\"]\n# TARGET_EXPERIMENTS = [\"./TC/Medium/8_odd_signal\"]\n# TARGET_EXPERIMENTS = [\"./TC/Medium/9_counting_signals\"]\n# TARGET_EXPERIMENTS = [\"./TC/Medium/10_half_adder\"]\n# TARGET_EXPERIMENTS = [\"./TC/Medium/11_full_adder\"]\n# TARGET_EXPERIMENTS = [\"./TC/Medium/16_adder_8bit\"]\n# TARGET_EXPERIMENTS = [\"./TC/Medium/17_mux_8bit\"]\n# TARGET_EXPERIMENTS = [\"./TC/Medium/18_opposite_number\"]\n# TARGET_EXPERIMENTS = [\"./TC/Medium/22_decoder_3bit\"]\n# TARGET_EXPERIMENTS = [\"./TC/Medium/27_instruction_decoder\"]\n\n# TARGET_EXPERIMENTS = []  # 可以指定要测试的特定文件夹的路径，留空则测试 EXPERIMENTS_ROOT 下的所有子文件夹\n# 日志配置\n"}}, "GUI/Gradio_ui_6.py": {"4677b755a718f5b15fc304ba3866a8e04a9a2902": {"t": 1785595586.0067935, "n": 58547, "h": "5d2563a7c5fb007e", "c": "# grad_ui_revised_workflow.py\nimport gradio as gr\nimport sys\nimport os\nimport logging\nimport re\nimport time\nimport html\nfrom datetime import datetime\nimport shutil\n\ntry:\n    import ollama\n\n    ollama_available = True\nexcept ImportError:\n    logging.warning(\"ollama module not found. Chat functionality will be limited.\")\n    ollama_available = False\n\n# --- AGENT_AVATARS and Logging Setup (保持不变) ---\nAGENT_AVATARS = {\n    \"UserProxy\": {\"path\": \"icons/user.png\", \"emoji\": \"😊\"}, \"CoderAgent\": {\"path\": \"icons/coder.png\", \"emoji\": \"💻\"},\n    \"Reviewer\": {\"path\": \"icons/reviewer.png\", \"emoji\": \"🔍\"}, \"Executor\": {\"path\": \"icons/executor.png\", \"emoji\": \"⚙️\"},\n    \"Summarizer\": {\"path\": \"icons/summarizer.png\", \"emoji\": \"📝\"}, \"root\": {\"path\": \"icons/root.png\", \"emoji\": \"🌟\"},\n    \"RAG\": {\"path\": \"icons/rag.png\", \"emoji\": \"📚\"}, \"Mediator\": {\"path\": \"icons/mediator.png\", \"emoji\": \"🤝\"},\n    \"ChatSession-coder\": {\"path\": \"icons/chatsession-coder.png\", \"emoji\": \"🖥️\"},\n    \"ChatSession-reviewer\": {\"path\": \"icons/chatsession-reviewer.png\", \"emoji\": \"🕵️\"},\n    \"transitions.core\": {\"path\": \"icons/transitions-core.png\", \"emoji\": \"🔄\"},\n    \"httpx\": {\"path\": \"icons/httpx.png\", \"emoji\": \"🌐\"}, \"default\": {\"path\": \"icons/default.png\", \"emoji\": \"❓\"}\n}\nlogging.basicConfig(stream=sys.stdout, level=logging.INFO,\n                    format='[%(asctime)s] [%(levelname)s] [%(name)s:%(lineno)d] %(message)s')\n\n\n# --- LogProcessor, format_chat_records_to_html, compare_simulation_results, extract_module_name (保持不变) ---\nclass LogProcessor:\n    def __init__(self, log_format_regex, agent_format_regex, timestamp_format_str=None,\n                 avatar_dir=\"avatars_gradio/resized\"):\n        self.log_format = re.compile(log_format_regex, re.MULTILINE)\n        self.agent_format = re.compile(agent_format_regex)\n        self.timestamp_format = timestamp_format_str\n        self.avatar_dir = avatar_dir\n        os.makedirs(self.avatar_dir, exist_ok=True)\n        self.color_code_regex = re.compile(r'\\x1B\\[[0-9;]*[mGKHfJABCD]?')\n        self.current_conversation = None;\n        self.current_agent = None;\n        self.current_timestamp = None;\n        self.current_log_level = None\n        self.message_buffer = [];\n        self.avatar_cache = {}\n\n    def remove_color_codes(self, text):\n        return self.color_code_regex.sub('', text)\n\n    def clean_message(self, message):\n        message = self.remove_color_codes(message); message = message.strip(); return message if message else None\n\n    def get_avatar(self, agent_name):  # Modified for Gradio file serving\n        avatar_info = AGENT_AVATARS.get(agent_name, AGENT_AVATARS[\"default\"])\n        avatar_path = avatar_info[\"path\"];\n        emoji = avatar_info[\"emoji\"]\n        if os.path.exists(avatar_path): return {\"type\": \"image\",\n                                                \"value\": os.path.abspath(avatar_path).replace(os.sep, '/'),\n                                                \"emoji\": emoji}\n        return {\"type\": \"emoji\", \"value\": emoji}\n\n    def flush_buffer(self, chat_records):\n        if not self.message_buffer or not self.current_agent: self.message_buffer = []; return\n        message = '\\n'.join(self.message_buffer);\n        cleaned_message = self.clean_message(message)\n        if cleaned_message:\n            formatted_time = self.current_timestamp or datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n            try:\n                if self.current_timestamp and self.timestamp_format:\n                    time_obj = datetime.strptime(self.current_timestamp, self.timestamp_format)\n                    formatted_time = time_obj.strftime('%Y-%m-%d %H:%M:%S')\n            except (ValueError, TypeError) as e:\n                logging.warning(f\"Timestamp parsing failed ({e}), using raw: {self.current_timestamp}\")\n            avatar = self.get_avatar(self.current_agent)\n            chat_records.append(\n                {'time': formatted_time, 'agent': self.current_agent, 'avatar': avatar, 'message': cleaned_message})\n        self.message_buffer = []\n\n    def parse_log_to_chat_records(self, log_file_path, last_position):\n        chat_records = [];\n        error_message = None;\n        new_position = last_position\n        if not log_file_path or not os.path.exists(\n            log_file_path): return chat_records, new_position, \"Log file does not exist.\"\n        if os.path.getsize(log_file_path) == 0: return chat_records, new_position, \"Log file is empty.\"\n        try:\n            with open(log_file_path, 'r', encoding='utf-8') as f:\n                f.seek(last_position);\n                content = f.read();\n                new_position = f.tell()\n            if not content: return chat_records, new_position, None\n            cleaned_content = self.remove_color_codes(content);\n            lines = cleaned_content.splitlines()\n        except Exception as e:\n            return chat_records, last_position, f\"Error reading log file: {str(e)}\"\n        for line in lines:\n            line = line.strip();\n            if not line: continue\n            match = self.log_format.match(line)\n            if match:\n                self.flush_buffer(chat_records);\n                log_data = match.groupdict()\n                timestamp = log_data.get('timestamp', '');\n                log_level = log_data.get('log_level', '').upper()\n                agent_name = log_data.get('agent_name', '');\n                message_part = log_data.get('message', '').strip()\n                if log_level not in ['DIALOGUE', 'INFO', 'DEBUG'] or not AGENT_AVATARS.get(agent_name):\n                    if self.current_agent and message_part: self.message_buffer.append(line)\n                    continue\n                self.current_agent = agent_name;\n                self.current_timestamp = timestamp;\n                self.current_log_level = log_level\n                if message_part: self.message_buffer.append(message_part)\n            elif self.current_agent:\n                self.message_buffer.append(line)\n        self.flush_buffer(chat_records);\n        return chat_records, new_position, error_message\n\n\ndef format_chat_records_to_html(chat_records_list):  # (保持不变)\n    if not chat_records_list: return \"<p>No agent interactions logged yet.</p>\"\n    html_parts = [\n        \"<style>body { font-family: Arial, sans-serif; margin: 0px; } .message-container { margin-bottom: 12px; display: flex; align-items: flex-start; padding: 5px;} .avatar-container { width: 32px; height: 32px; margin-right: 8px; flex-shrink: 0; display:flex; align-items:center; justify-content:center; } .avatar-img { width: 30px; height: 30px; border-radius: 50%; } .avatar-emoji { font-size: 24px; } .content-container { display: flex; flex-direction: column; max-width: calc(100% - 50px); } .header { display: flex; align-items: center; margin-bottom: 3px; } .agent-name { font-weight: bold; margin-right: 8px; font-size: 0.9em; color: #333; } .timestamp { color: #777; font-size: 0.8em; } .bubble { background-color: #f0f0f0; border-radius: 8px; padding: 8px 10px; font-size: 0.95em; color: #333; white-space: pre-wrap; word-wrap: break-word; box-shadow: 0 1px 1px rgba(0,0,0,0.05); } </style><div>\"]\n    for record in chat_records_list:\n        escaped_message = html.escape(record['message']);\n        agent_name_html = html.escape(record['agent']);\n        timestamp_html = html.escape(record['time']);\n        avatar_data = record.get('avatar', {})\n        html_parts.append('<div class=\"message-container\"><div class=\"avatar-container\">')\n        if avatar_data.get('type') == 'image' and avatar_data.get('value') and os.path.exists(\n                avatar_data['value'].replace('file:///', '')):  # Check local path if using file:///\n            # Gradio's `file=` prefix is for absolute paths.\n            # The path from get_avatar is already absolute and /-formatted.\n            html_parts.append(f'<img src=\"file={avatar_data[\"value\"]}\" class=\"avatar-img\" alt=\"{agent_name_html}\">')\n        else:\n            emoji_val = avatar_data.get('value', AGENT_AVATARS[\"default\"][\"emoji\"])\n            html_parts.append(f'<span class=\"avatar-emoji\">{emoji_val}</span>')\n        html_parts.append('</div><div class=\"content-container\"><div class=\"header\">')\n        html_parts.append(\n            f'<span class=\"agent-name\">{agent_name_html}</span><span class=\"timestamp\">[{timestamp_html}]</span></div>')\n        html_parts.append(f'<div class=\"bubble\">{escaped_message}</div></div></div>')\n    html_parts.append(\"</div>\");\n    return \"\".join(html_parts)\n\n\ndef compare_simulation_results(actual_output, expected_output):  # (保持不变)\n    actual_lines = actual_output.strip().split('\\n');\n    actual_results = []\n    for line in actual_lines:\n        match = re.match(r\"Time=\\d+:\\s*in=(\\d),\\s*out=(\\d)\", line.strip())\n        if match: actual_results.append((match.group(1), match.group(2)))\n    expected_lines = expected_output.strip().split('\\n');\n    expected_results = []\n    for line in expected_lines:\n        match = re.match(r\"Time=\\d+:\\s*in=(\\d),\\s*out=(\\d)\", line.strip())\n        if match: expected_results.append((match.group(1), match.group(2)))\n    if not actual_results and not expected_results and actual_output.strip() == expected_output.strip(): return True, \"Outputs match (non-structured).\"\n    if not actual_results and not expected_results and actual_output.strip() != expected_output.strip(): return False, f\"Output format mismatch or one output is empty. Actual: {len(actual_results)} structured, Expected: {len(expected_results)} structured.\"\n    if not actual_results and expected_results: return False, \"Actual output empty/wrong format, expected has data.\"\n    if actual_results and not expected_results: return False, \"Expected output empty/wrong format, actual has data.\"\n    if len(actual_results) != len(\n        expected_results): return False, f\"Output length mismatch: expected {len(expected_results)}, got {len(actual_results)}\"\n    for i, (actual, expected) in enumerate(zip(actual_results, expected_results)):\n        if actual != expected: return False, f\"Mismatch at line {i + 1}: expected in={expected[0]}, out={expected[1]}; got in={actual[0]}, out={actual[1]}\"\n    return True, \"All structured tests passed.\"\n\n\ndef extract_module_name(verilog_code):  # (保持不变)\n    if not verilog_code: return None\n    match = re.search(r'module\\s+(\\w+)\\s*\\(', verilog_code);\n    return match.group(1) if match else None\n\n\n# --- Configuration Module (MockConfigModule - 保持不变，但其属性将被正确使用) ---\nclass MockConfigModule:\n    EXPERIMENTS_ROOT = \"./experiments_gradio\";\n    OPENAI_API_KEY = \"\";\n    KNOWLEDGE_BASE_PATH = './knowledge_base/RAG-data'\n    DETAIL_KNOWLEDGE_BASE_PATH = './knowledge_base/RAG-data-detail';\n    EMBEDDING_MODEL = 'nomic-embed-text';\n    RAG_LLM_MODEL = \"\"\n    RAG_INDEX_PATH = './knowledge_base/RAG-data/vector_index.faiss';\n    RAG_DATA_PATH = './knowledge_base/RAG-data/vector_data.json'\n    SUMMARY_JSON_PATH_TEMPLATE = \"./knowledge_base/Model-incrementment/{model_name}.json\"\n    VERILOG_DIR = \"\";\n    TESTBENCH_PATH = \"\";\n    REFERENCE_CODE_PATH = None;\n    SUMMARY_FILE = \"\";\n    EXPERIMENT_LOG_FILE = \"\"\n    api_configs = {\n        \"glm-4-plus\": {\n            \"API_KEY\": [\"780fce9dabb5a1005e38baf6b6e556d8.VCLi2kOdjLs6DQtM\"],\n            \"BASE_URL\": \"https://open.bigmodel.cn/api/paas/v4/\",\n        },\n        \"deepseek-chat\": {\n            \"API_KEY\": [\"sk-f73a42bc3f1643b69059757101005d0d\"],\n            \"BASE_URL\": \"https://api.deepseek.com\",\n        },\n        \"gpt-4o\": {\n            \"API_KEY\": [\n                \"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\"],\n            \"BASE_URL\": \"https://api.openai.com/v1\",\n        },\n        \"gpt-4o-mini\": {\n            \"API_KEY\": [\n                \"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\"],\n            \"BASE_URL\": \"https://api.openai.com/v1\",\n        },\n        \"glm-4-air\": {\n            \"API_KEY\": [\"780fce9dabb5a1005e38baf6b6e556d8.VCLi2kOdjLs6DQtM\"],\n            \"BASE_URL\": \"https://open.bigmodel.cn/api/paas/v4/\",\n        },\n        \"qwq:latest\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.148.206:11434/v1\",\n        },\n        \"qwq:32b\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.149.18:11434/v1\",\n        },\n\n        \"qwq:32b-q8_0\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.144.196:11434/v1\",\n        },\n        \"qwen2.5-coder:14b\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.149.18:11434/v1\",\n        },\n        \"qwen2.5:14b\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.144.196:11434/v1\",\n        },\n        \"deepseek-coder:33b\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.148.206:11434/v1\",\n        },\n        \"deepseek-coder:6.7b\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.149.18:11436/v1\",\n        },\n        \"qwen2.5:7b\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://localhost:11434/v1\",\n        },\n        \"qwen2.5:7b-instruct-fp16\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.148.206:11434/v1\",\n        },\n        \"qwen2.5-coder:32b\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.148.206:11436/v1\",\n        },\n        \"deepseek-ai/DeepSeek-V3\": {\n            \"API_KEY\": [\"sk-ptyrqcoakodsijdqglxbzucezfvdmdolzxxfafdpozixljdr\"],\n            \"BASE_URL\": \"https://api.siliconflow.cn/v1\"\n        },\n        \"deepseek-r1-250120\": {\n            \"API_KEY\": [\"009039c2-755b-4abd-8d76-225b3456ea29\", \"0a12f045-e34a-46b2-b3a8-999ff9342fcc\",\n                        \"137d25a5-b809-4a3c-a2ec-22d984d653df\", \"4ac99dcf-cf55-4e56-b25c-879d80e59e2c\", ],\n            \"BASE_URL\": \"https://ark.cn-beijing.volces.com/api/v3/\"\n        },\n        \"deepseek-r1\": {\n            \"API_KEY\": [\"sk-da2iftmgizerrfjj\"],\n            \"BASE_URL\": \"https://cloud.infini-ai.com/maas/v1\"\n        },\n        \"deepseek-r1-distill-qwen-32b\": {\n            \"API_KEY\": [\"sk-da2jurhjtunkwnuz\"],\n            \"BASE_URL\": \"https://cloud.infini-ai.com/maas/v1\"\n        },\n        \"qwq-32b\": {\n            \"API_KEY\": [\"sk-da2juiy2cochoavl\"],\n            \"BASE_URL\": \"https://cloud.infini-ai.com/maas/v1\"\n        },\n        \"deepseek-v3\": {\n            \"API_KEY\": [\"sk-da2iftmgizerrfjj\"],\n            \"BASE_URL\": \"https://cloud.infini-ai.com/maas/v1\"\n        },\n        \"qwen2.5-coder-32b-instruct\": {\n            \"API_KEY\": [\"sk-da2jurhjtunkwnuz\"],\n            \"BASE_URL\": \"https://cloud.infini-ai.com/maas/v1\"\n        },\n        \"deepseek-v3-250324\": {\n            \"API_KEY\": [\"e99646c6-b52a-45a2-ad49-c17415174d8d\"],\n            \"BASE_URL\": \"https://ark.cn-beijing.volces.com/api/v3/\"\n        },\n        \"deepseek-r1:70b\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.144.196:11434/v1\",\n        },\n        \"phi4:latest\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.144.196:11434/v1\",\n        },\n        \"deepseek-r1:32b\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.144.208:11437/v1\",\n        },\n        \"deepseek-r1:14b\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.144.208:11436/v1\",\n        },\n\n        \"gemini-2.0-flash\": {\n            \"API_KEY\": [\"AIzaSyB0PscblS6t4TSreKYY3uFPMHJb1nwA4b0\", \"AIzaSyAWmeLGAx9RctoqbLlaHt__g747P3Bdg3s\", ],\n            \"BASE_URL\": \"https://generativelanguage.googleapis.com/v1beta/\"\n        },\n        \"gemini-2.0-flash-thinking-exp\": {\n            \"API_KEY\": [\n                \"AIzaSyCe1FCm51LdQGv_lQalEeYwz7OsEfwauwg\",\n                \"AIzaSyCsdInwWPMIFMJ3vQJfiTKRZPnsRmLZgJY\",\n                \"AIzaSyCVpmMEKNuRJRqVzCWmVfUKfFnbKZitYxI\",\n                \"AIzaSyDp_C9d7fxlPw2n71vokC7zbV1AIOsp-zM\",\n                \"AIzaSyCATsoE4Luq3AtoZIUbzyPZVFHPeWEn1QU\",\n                \"AIzaSyBO13wFHgRgDtdw_vKtX853q_zMdmTBPV8\",\n                \"AIzaSyBhdQ6MRWk5wkxFKOrABAXDFaBGVWIzOXs\",\n                \"AIzaSyAWmeLGAx9RctoqbLlaHt__g747P3Bdg3s\",\n                \"AIzaSyD5xok4XZST4m4kaxRZPxtyrBFaFgTN7jM\",\n                \"AIzaSyCYfvOUl8sCo8spAtq4Oe20ZFIiCJyHtY8\",\n                \"AIzaSyDqQNRPlppxGW_m6bSS6lcPdAZzWZpbEGQ\",\n                \"AIzaSyCRMMkCtRnAFj5E3X5H1lnIq5HeJXKhqpw\",\n                \"AIzaSyCvs-dZ-oO-T3NAYMAC2PzEbmUz824WJds\",\n                \"AIzaSyB2NYBW-cQ8KPE_ksMO5qZRFHRJ8GOXpoE\",\n                \"AIzaSyCogr95x1PyNeHNuG-WK7Bkw9piSyRbMEY\",\n                \"AIzaSyCxxCys9WVW3FuPgRWzomXOhGEqgPTenDA\",\n            ],\n            \"BASE_URL\": \"https://generativelanguage.googleapis.com/v1beta/\"\n        },\n\n        \"gemini-2.0-pro-exp\": {\n            \"API_KEY\": [\n                \"AIzaSyCe1FCm51LdQGv_lQalEeYwz7OsEfwauwg\",\n                \"AIzaSyCxxCys9WVW3FuPgRWzomXOhGEqgPTenDA\",\n                \"AIzaSyCsdInwWPMIFMJ3vQJfiTKRZPnsRmLZgJY\",\n                \"AIzaSyCVpmMEKNuRJRqVzCWmVfUKfFnbKZitYxI\",\n                \"AIzaSyDp_C9d7fxlPw2n71vokC7zbV1AIOsp-zM\",\n                \"AIzaSyCATsoE4Luq3AtoZIUbzyPZVFHPeWEn1QU\",\n                \"AIzaSyBO13wFHgRgDtdw_vKtX853q_zMdmTBPV8\",\n                \"AIzaSyBhdQ6MRWk5wkxFKOrABAXDFaBGVWIzOXs\",\n                \"AIzaSyAWmeLGAx9RctoqbLlaHt__g747P3Bdg3s\",\n                \"AIzaSyD5xok4XZST4m4kaxRZPxtyrBFaFgTN7jM\",\n                \"AIzaSyCYfvOUl8sCo8spAtq4Oe20ZFIiCJyHtY8\",\n                \"AIzaSyDqQNRPlppxGW_m6bSS6lcPdAZzWZpbEGQ\",\n                \"AIzaSyCRMMkCtRnAFj5E3X5H1lnIq5HeJXKhqpw\",\n                \"AIzaSyCvs-dZ-oO-T3NAYMAC2PzEbmUz824WJds\",\n                \"AIzaSyB2NYBW-cQ8KPE_ksMO5qZRFHRJ8GOXpoE\",\n                \"AIzaSyCogr95x1PyNeHNuG-WK7Bkw9piSyRbMEY\",\n            ],\n            \"BASE_URL\": \"https://generativelanguage.googleapis.com/v1beta/\"\n        },\n        \"gemini-2.0-flash-exp\": {\n\n            \"API_KEY\": [\n                \"AIzaSyCsdInwWPMIFMJ3vQJfiTKRZPnsRmLZgJY\",\n                \"AIzaSyCVpmMEKNuRJRqVzCWmVfUKfFnbKZitYxI\",\n                \"AIzaSyDp_C9d7fxlPw2n71vokC7zbV1AIOsp-zM\",\n                \"AIzaSyCATsoE4Luq3AtoZIUbzyPZVFHPeWEn1QU\",\n                \"AIzaSyBO13wFHgRgDtdw_vKtX853q_zMdmTBPV8\",\n                \"AIzaSyBhdQ6MRWk5wkxFKOrABAXDFaBGVWIzOXs\",\n                \"AIzaSyAWmeLGAx9RctoqbLlaHt__g747P3Bdg3s\",\n                \"AIzaSyD5xok4XZST4m4kaxRZPxtyrBFaFgTN7jM\",\n                \"AIzaSyCYfvOUl8sCo8spAtq4Oe20ZFIiCJyHtY8\",\n                \"AIzaSyDqQNRPlppxGW_m6bSS6lcPdAZzWZpbEGQ\",\n                \"AIzaSyCRMMkCtRnAFj5E3X5H1lnIq5HeJXKhqpw\",\n                \"AIzaSyCvs-dZ-oO-T3NAYMAC2PzEbmUz824WJds\",\n                \"AIzaSyB2NYBW-cQ8KPE_ksMO5qZRFHRJ8GOXpoE\",\n                \"AIzaSyCogr95x1PyNeHNuG-WK7Bkw9piSyRbMEY\",\n                \"AIzaSyCe1FCm51LdQGv_lQalEeYwz7OsEfwauwg\",\n                \"AIzaSyCxxCys9WVW3FuPgRWzomXOhGEqgPTenDA\",\n            ],\n\n            \"BASE_URL\": \"https://generativelanguage.googleapis.com/v1beta/\"\n        },\n        \"grok-3-latest\": {\n            \"API_KEY\": [\"xai-PRXLUHFvDdVPqNunuWOp2aGrG6h520hR3xXq5WiriowRlONwtbkC6bN3VcCDRw2ROBj8M23IwaQBMCiS\"],\n            \"BASE_URL\": \"https://api.x.ai/v1\"\n        },\n        \"RV-Qwen3-S1\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.144.196:11434/v1\",\n        }\n\n    }\n    default_model = \"qwen2.5-coder:14b\"\n\n    def __init__(self):\n        os.makedirs(self.EXPERIMENTS_ROOT, exist_ok=True)\n        if self.SUMMARY_JSON_PATH_TEMPLATE and \"{model_name}\" in self.SUMMARY_JSON_PATH_TEMPLATE:  # Ensure dirname works\n            summary_dir = os.path.dirname(self.SUMMARY_JSON_PATH_TEMPLATE.format(model_name=\"any_model\"))\n            if summary_dir: os.makedirs(summary_dir, exist_ok=True)\n        if self.KNOWLEDGE_BASE_PATH: os.makedirs(self.KNOWLEDGE_BASE_PATH, exist_ok=True)\n        self.RAG_LLM_MODEL = self.default_model\n        self.OPENAI_API_KEY = self.get_current_api_key_value(self.default_model)  # Store actual key string or list\n\n    def get_current_api_key_value(self, model_name_str):  # Renamed to avoid confusion with original\n        return self.api_configs.get(model_name_str, {}).get(\"API_KEY\", \"\")  # Use \"API_KEY\" as in your config\n\n    def update_llm_settings(self, model_name, api_key_override_value):\n        self.RAG_LLM_MODEL = model_name\n        if api_key_override_value:  # Assuming api_key_override_value is the key string itself\n            self.OPENAI_API_KEY = api_key_override_value\n            logging.info(f\"Config updated: Workflow Model={model_name}. Using OVERRIDE API Key.\")\n        else:\n            self.OPENAI_API_KEY = self.get_current_api_key_value(model_name)\n            logging.info(\n                f\"Config updated: Workflow Model={model_name}. Using configured API Key: {'********' if self.OPENAI_API_KEY else 'None/NotNeeded'}.\")\n\n\nconfig_module = MockConfigModule()\n\n\n# --- Dummy Backend Classes (修改 AgentDummy 以更好地反映 PyQt5 WorkflowWorker 中的 Agent 角色) ---\n# PyQt5 WorkflowWorker 使用 self.UserProxy_class, self.CoderAgent_class 等,\n# 它们在 ImportError 时都指向同一个 _dummy_agent 类型。\n# 我们用 AgentDummy 代表这个通用的 _dummy_agent 类型。\n# 关键在于 run_workflow_gradio 中如何实例化和使用它来代表不同的角色。\n\nclass MediatorDummy:  # (与之前相同)\n    def __init__(self): self._latest_code = None; self._latest_sim_result = None; self._summary = None; logging.info(\n        \"MediatorDummy initialized.\")\n\n    def get_latest_code(self, agent_name=\"\", message=\"\"): return self._latest_code\n\n    def get_latest_simulation_result(self, agent_name=\"\", message=\"\"): return self._latest_sim_result\n\n    def set_code(self, code, agent=\"CoderAgent\"): self._latest_code = code; log_agent_activity(agent,\n                                                                                               f\"Submitted code snippet:\\n{code[:100]}...\")\n\n    def set_simulation_result(self, result, agent=\"Executor\"): self._latest_sim_result = result; log_agent_activity(\n        agent, f\"Submitted simulation result: {result.get('message', 'No message')}\")\n\n    def set_summary(self, summary_text, agent=\"Summarizer\"): self._summary = summary_text; log_agent_activity(agent,\n                                                                                                              f\"Submitted summary: {summary_text[:100]}...\")\n\n    def get_summary(self): return self._summary\n\n\nclass AgentDummy:  # 代表 PyQt5 中的 _dummy_agent\n    def __init__(self, name, mediator, config, rag_system=None):\n        self.name = name\n        self.mediator = mediator\n        self.config = config  # This is the config_module instance\n        self.rag_system = rag_system\n        # Agent 知道它应该使用的模型和 API 密钥来自 self.config\n        logging.info(\n            f\"AgentDummy '{self.name}' initialized. Configured LLM for workflow: '{self.config.RAG_LLM_MODEL}'.\")\n        log_agent_activity(self.name, f\"Initialized (Workflow LLM: {self.config.RAG_LLM_MODEL})\")\n\n    def submit_design_request(self, prompt_or_data):\n        # 这是 UserProxy 的主要入口点，或者其他 Agent 处理特定任务的方法。\n        # 在 PyQt5 WorkflowWorker.run() 中，首先调用 user_proxy.submit_design_request(self.prompt)\n        # 这个调用启动了整个流程。\n        # 对于 Gradio，我们将模拟这个启动，然后逐步执行其他 Agent 的步骤。\n        log_agent_activity(self.name, f\"Received task/prompt: '{str(prompt_or_data)[:70]}...'\")\n\n        # ---- 这个 AgentDummy 的行为需要根据其 'name' 来模拟 PyQt5 中特定 Agent 的行为 ----\n        # ---- 或者，更准确地，run_workflow_gradio 将显式调用代表特定 Agent 的方法 ----\n\n        if self.name == \"UserProxy\":\n            # UserProxy 通常只是启动流程，具体工作由其他 Agent 完成。\n            # 在 PyQt5 中，它调用 self.config_module.update_config 等。\n            # 这里我们假设它完成了初始设置，并可能将任务传递给 CoderAgent（通过 Mediator 或直接）。\n            log_agent_activity(self.name, \"Design request submitted. Forwarding to CoderAgent.\")\n            # 实际中，这里可能没有返回值，而是通过 Mediator 驱动后续流程。\n            # 为了让 Gradio 的流程更明确，我们让 run_workflow_gradio 来编排。\n            return \"UserProxy: Design request initiated.\"\n\n        elif self.name == \"CoderAgent\":\n            log_agent_activity(self.name,\n                               f\"Generating code based on: '{str(prompt_or_data)[:50]}...' using LLM {self.config.RAG_LLM_MODEL}\")\n            time.sleep(0.5)  # 模拟 LLM 调用\n            generated_code = f\"// Generated by CoderAgent with {self.config.RAG_LLM_MODEL}\\n\" \\\n                             f\"module design_from_{self.name} (input clk, input rst_n);\\n\" \\\n                             f\"  // Prompt: {str(prompt_or_data)[:30]}...\\nendmodule\"\n            self.mediator.set_code(generated_code, agent=self.name)\n            return generated_code\n\n        elif self.name == \"Reviewer\":\n            code_to_review = prompt_or_data  # 假设 Reviewer 接收代码作为输入\n            log_agent_activity(self.name,\n                               f\"Reviewing code (first 50 chars): '{str(code_to_review)[:50]}...' using LLM {self.config.RAG_LLM_MODEL}\")\n            time.sleep(0.3)\n            review_comment = f\"Reviewer {self.name}: Code looks acceptable (dummy review).\"\n            log_agent_activity(self.name, review_comment)\n            # Reviewer 可能更新 Mediator 中的代码或添加审查意见\n            # self.mediator.add_review_comment(review_comment, agent=self.name)\n            return review_comment  # 或者返回被审查的代码/状态\n\n        elif self.name == \"Executor\":\n            # Executor 接收 Verilog 代码和 Testbench 路径\n            # prompt_or_data 在这里应该是包含代码和 testbench_path 的字典或元组\n            # 为了简单，我们假设它从 Mediator 获取代码，testbench_path 从 config_module 获取\n            code_to_simulate = self.mediator.get_latest_code()\n            testbench_path = self.config.TESTBENCH_PATH  # 已在 setup_environment_dummy 中设置\n            log_agent_activity(self.name,\n                               f\"Executing simulation for code with testbench: {os.path.basename(testbench_path)}\")\n            time.sleep(0.5)\n\n            current_experiment_path = os.path.dirname(testbench_path)\n            exp_out_path = os.path.join(current_experiment_path, \"expected_output.txt\")\n            expected_output_content = \"Default Expected: Time=0: in=0, out=1\\nTime=10: in=1, out=0\"\n            if os.path.exists(exp_out_path):\n                with open(exp_out_path, 'r', encoding='utf-8') as f:\n                    expected_output_content = f.read().strip()\n            else:\n                log_agent_activity(self.name, f\"Warning: Expected output file not found at {exp_out_path}\")\n\n            sim_passed = \"simple_pass_condition\" in str(code_to_simulate).lower()  # Dummy pass/fail\n            actual_sim_output = expected_output_content if sim_passed else \"Executor Sim: Time=0: in=0, out=0\\nTime=10: in=1, out=1\"\n            sim_result_msg = \"All tests passed (Simulated by Executor).\" if sim_passed else \"Simulation failed (Simulated by Executor).\"\n            sim_data = {\"execution_result\": actual_sim_output, \"message\": sim_result_msg,\n                        \"expected_output_used\": expected_output_content}\n            self.mediator.set_simulation_result(sim_data, agent=self.name)\n            return sim_data\n\n        elif self.name == \"Summarizer\":\n            # Summarizer 可能从 Mediator 获取所有相关信息\n            final_code = self.mediator.get_latest_code()\n            sim_results = self.mediator.get_latest_simulation_result()\n            # metrics_data = self.mediator.get_metrics() # 假设 Mediator 也存储指标\n\n            log_agent_activity(self.name, f\"Summarizing results using LLM {self.config.RAG_LLM_MODEL}...\")\n            time.sleep(0.2)\n            summary = f\"Summary by {self.name} (LLM: {self.config.RAG_LLM_MODEL}):\\n\"\n            summary += f\"- Code: {'Available' if final_code else 'N/A'}.\\n\"\n            summary += f\"- Simulation: {sim_results.get('message', 'N/A') if sim_results else 'N/A'}\\n\"\n            # summary += f\"- Metrics: {str(metrics_data)[:50]}...\\nWorkflow concluded.\"\n            summary += \"Workflow concluded (dummy summary).\"\n            self.mediator.set_summary(summary, agent=self.name)\n            log_agent_activity(self.name, \"Summary generated.\")\n            return summary\n        else:\n            log_agent_activity(self.name, \"Unknown task or role.\")\n            return \"Agent action not defined for this role in dummy.\"\n\n\n# --- setup_environment_dummy, initialize_rag_system_dummy, analyze_verilog_dummy (保持不变) ---\ndef setup_environment_dummy(experiment_path, testbench_src_path, expected_out_src_path, prompt_content):\n    logging.info(f\"Setting up dummy environment in: {experiment_path}\")\n    os.makedirs(experiment_path, exist_ok=True)\n    verilog_dir = os.path.join(experiment_path, \"generated_verilog\");\n    os.makedirs(verilog_dir, exist_ok=True)\n    config_module.VERILOG_DIR = verilog_dir\n    testbench_dest_path = os.path.join(experiment_path, \"testbench_ref.v\")\n    if testbench_src_path and os.path.exists(testbench_src_path):\n        shutil.copy(testbench_src_path, testbench_dest_path)\n    else:\n        logging.warning(f\"Testbench source file not found at {testbench_src_path}, creating dummy.\");\n        with open(testbench_dest_path, \"w\") as f:\n            f.write(\"// Dummy testbench file\\n\")\n    config_module.TESTBENCH_PATH = testbench_dest_path\n    if expected_out_src_path and os.path.exists(expected_out_src_path):\n        shutil.copy(expected_out_src_path, os.path.join(experiment_path, \"expected_output.txt\"))\n    else:\n        logging.warning(f\"Expected output source file not found at {expected_out_src_path}.\")\n    with open(os.path.join(experiment_path, \"prompt.txt\"), \"w\", encoding=\"utf-8\") as f:\n        f.write(prompt_content)\n    config_module.SUMMARY_FILE = os.path.join(experiment_path, \"summary.json\")\n    config_module.EXPERIMENT_LOG_FILE = os.path.join(experiment_path, \"experiment_run.log\")\n    with open(config_module.EXPERIMENT_LOG_FILE, \"w\", encoding=\"utf-8\") as f:\n        f.write(f\"[{datetime.now().strftime('%Y-%m-%d %H:%M:%S,%f')[:-3]}] [INFO] [System] Log initialized.\\n\")\n    return (verilog_dir, testbench_dest_path, None, config_module.SUMMARY_FILE, experiment_path)\n\n\ndef initialize_rag_system_dummy(*args_from_config):  # 接收来自 config_module 的参数\n    logging.info(\n        f\"Initializing dummy RAG system with KNOWLEDGE_BASE_PATH: {args_from_config[0]}, RAG_LLM_MODEL: {args_from_config[3]}\")\n    return \"rag_system_instance_dummy\"\n\n\ndef analyze_verilog_dummy(verilog_code, module_name, model=\"QWQ\"):  # (保持不变)\n    logging.info(f\"Analyzing Verilog (dummy) for module: {module_name}\");\n    if not verilog_code or not module_name: return {\"error\": \"No code/module for analysis.\"}\n    return {\"logic_gate_count\": 10, \"longest_delay\": 5.0, \"longest_path\": [\"A\", \"B\", \"C\"]}\n\n\n# --- GradioAppState, log_agent_activity (保持不变) ---\nclass GradioAppState:\n    def __init__(self):\n        self.current_log_file_path = None;\n        self.current_log_last_pos = 0\n        self.log_processor = LogProcessor(\n            r'^\\[(?P<timestamp>\\d{4}-\\d{2}-\\d{2} \\d{2}:\\d{2}:\\d{2},\\d{3})\\] \\[(?P<log_level>.*?)\\] \\[(?P<agent_name>.*?)\\]\\s*(?P<message>.*)',\n            r'^\\s*, agent=(?P<agent_name>[^\\s,]+)(?:, message=(?P<message>.*))?$', '%Y-%m-%d %H:%M:%S,%f')\n        self.agent_interaction_records = [];\n        self.testbench_file_path = None;\n        self.expected_output_file_path = None\n        self.active_model_name = config_module.default_model;\n        self.active_api_key = config_module.get_current_api_key_value(config_module.default_model)\n\n\napp_state = GradioAppState()\n\n\ndef log_agent_activity(agent_name, message):\n    log_file_to_use = app_state.current_log_file_path\n    if log_file_to_use and os.path.exists(os.path.dirname(log_file_to_use)):\n        timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S,%f')[:-3]\n        log_entry = f\"[{timestamp}] [DIALOGUE] [{agent_name}] {message}\\n\"\n        try:\n            with open(log_file_to_use, \"a\", encoding=\"utf-8\") as f:\n                f.write(log_entry)\n        except Exception as e:\n            logging.error(f\"Failed to write log to {log_file_to_use}: {e}\")\n    else:\n        logging.info(f\"Log (no file: {log_file_to_use}): [{agent_name}] {message}\")\n\n\n# --- Gradio UI Definition (保持不变) ---\nwith gr.Blocks(theme=gr.themes.Soft(), title=\"VeriOptima (Gradio)\") as demo:\n    gr.Markdown(\"# VeriOptima: Verilog Optimization & Chat Agent\")\n    with gr.Row(equal_height=False):  # Global selectors\n        shared_model_selector = gr.Dropdown(label=\"Select LLM Model (Global)\",\n                                            choices=list(config_module.api_configs.keys()),\n                                            value=app_state.active_model_name, interactive=True)\n        shared_api_key_input = gr.Textbox(label=\"API Key (Global, overrides if set)\",\n                                          placeholder=\"Enter API key for selected model\",\n                                          value=app_state.active_api_key, type=\"password\", interactive=True)\n    gr.HTML(\"<hr>\")\n    with gr.Tabs(selected=0) as tabs:\n        with gr.TabItem(\"CircuitMind (Design Optimization)\", id=0):\n            with gr.Row():\n                with gr.Column(scale=2):  # Inputs\n                    design_prompt_input = gr.Textbox(lines=5, label=\"Design Requirements / Verilog to Optimize\",\n                                                     placeholder=\"Enter design prompt or Verilog code...\")\n                    with gr.Row(): design_testbench_file = gr.File(\n                        label=\"Upload Testbench File (.v)\"); design_expected_output_file = gr.File(\n                        label=\"Upload Expected Output File (.txt)\")\n                    with gr.Row(): design_testbench_preview = gr.Textbox(label=\"Testbench Preview\", lines=8,\n                                                                         interactive=False,\n                                                                         max_lines=10); design_expected_output_preview = gr.Textbox(\n                        label=\"Expected Output Preview\", lines=8, interactive=False, max_lines=10)\n                    design_run_button = gr.Button(\"Run Optimization Workflow\", variant=\"primary\")\n                with gr.Column(scale=1):  # Outputs\n                    generated_verilog_display = gr.Code(label=\"Generated/Optimized Verilog Code\", language=None,\n                                                        interactive=False)  # language=None for plaintext\n                    gate_metrics_display = gr.Textbox(label=\"Gate Metrics & Analysis\", lines=5, interactive=False)\n                    workflow_status_display = gr.Textbox(label=\"Workflow Status & Main Results\", lines=5,\n                                                         interactive=False)\n            gr.Markdown(\"### Agent Interactions Log\");\n            agent_log_display_html = gr.HTML(value=\"<p>Agent interactions will appear here...</p>\")\n        with gr.TabItem(\"ReasoningV (Chat with LLM)\", id=1):  # Chat UI (保持不变)\n            chat_display = gr.Chatbot(label=\"Chat with ReasoningV\", height=500)\n            chat_message_input = gr.Textbox(label=\"Your Message\", placeholder=\"Type your message and press Enter...\")\n            chat_send_button = gr.Button(\"Send\", variant=\"primary\")\n            gr.Markdown(\"### Extracted Verilog from Chat\");\n            chat_extracted_verilog_display = gr.Code(label=\"Verilog Code from Conversation\", language=None,\n                                                     interactive=False)\n            chat_optimize_button = gr.Button(\"Optimize this Verilog in CircuitMind\", visible=False)\n\n\n    # --- Event Handlers & Backend Logic ---\n    def update_active_llm_settings_globally(model_name, api_key_from_ui):  # (保持不变)\n        app_state.active_model_name = model_name\n        app_state.active_api_key = api_key_from_ui if api_key_from_ui else config_module.get_current_api_key_value(\n            model_name)\n        config_module.update_llm_settings(app_state.active_model_name, app_state.active_api_key)\n        logging.info(\n            f\"Global LLM settings updated: Model={app_state.active_model_name}, API Key Ready: {'********' if app_state.active_api_key else 'None/NotNeeded'}\")\n        return {\n            shared_api_key_input: app_state.active_api_key if api_key_from_ui else config_module.get_current_api_key_value(\n                model_name)}\n\n\n    shared_model_selector.change(update_active_llm_settings_globally,\n                                 inputs=[shared_model_selector, shared_api_key_input], outputs=[shared_api_key_input])\n    shared_api_key_input.change(update_active_llm_settings_globally,\n                                inputs=[shared_model_selector, shared_api_key_input], outputs=[shared_api_key_input])\n\n\n    def handle_file_upload_to_preview(file_obj, file_type_label):  # (保持不变)\n        content_preview = f\"No {file_type_label} file selected or error.\"\n        if file_obj:\n            if file_type_label == \"Testbench\":\n                app_state.testbench_file_path = file_obj.name\n            elif file_type_label == \"Expected Output\":\n                app_state.expected_output_file_path = file_obj.name\n            try:\n                with open(file_obj.name, \"r\", encoding=\"utf-8\") as f:\n                    content = f.read(2000)\n                content_preview = content if content else f\"{file_type_label} empty.\"\n            except Exception as e:\n                content_preview = f\"Error previewing: {e}\"\n        return content_preview\n\n\n    design_testbench_file.upload(lambda f: handle_file_upload_to_preview(f, \"Testbench\"),\n                                 inputs=[design_testbench_file], outputs=[design_testbench_preview])\n    design_expected_output_file.upload(lambda f: handle_file_upload_to_preview(f, \"Expected Output\"),\n                                       inputs=[design_expected_output_file], outputs=[design_expected_output_preview])\n\n\n    # # === UPDATED run_workflow_gradio to follow PyQt5 WorkflowWorker agent sequence ===\n    # def run_workflow_gradio(prompt, tb_file_dummy, exp_out_file_dummy, current_model_for_run, current_api_key_for_run,\n    #                         progress=gr.Progress(track_tqdm=True)):\n    #     # 0. Validation and Initial UI Update (保持不变)\n    #     if not prompt: yield {workflow_status_display: \"Error: Design prompt required.\",\n    #                           design_run_button: gr.update(interactive=True)}; return\n    #     tb_src_path = app_state.testbench_file_path\n    #     exp_out_src_path = app_state.expected_output_file_path\n    #     if not tb_src_path: yield {workflow_status_display: \"Error: Testbench file required.\",\n    #                                design_run_button: gr.update(interactive=True)}; return\n    #\n    #     config_module.update_llm_settings(current_model_for_run, current_api_key_for_run)\n    #     app_state.agent_interaction_records = [];\n    #     app_state.current_log_last_pos = 0\n    #     yield {generated_verilog_display: \"\", gate_metrics_display: \"\", workflow_status_display: \"Starting workflow...\",\n    #            agent_log_display_html: \"<p>Workflow initializing...</p>\",\n    #            design_run_button: gr.update(interactive=False)}\n    #\n    #     # 1. Setup Environment (与 PyQt5 WorkflowWorker.run() 中类似)\n    #     experiment_name = f\"gradio_exp_{datetime.now().strftime('%Y%m%d_%H%M%S')}\"  # Original used \"gui_experiment\", making it unique\n    #     experiment_path = os.path.join(config_module.EXPERIMENTS_ROOT, experiment_name)\n    #     progress.update(0.05)  # 移除 desc\n    #\n    #     # --- File copying (参照 PyQt5 WorkflowWorker) ---\n    #     # setup_environment_dummy will also copy these from tb_src_path and exp_out_src_path\n    #     # This explicit copy here is more for directness if setup_environment_dummy doesn't do it.\n    #     # For now, setup_environment_dummy handles copying from these source paths.\n    #\n    #     # --- config_module setup (参照 PyQt5 WorkflowWorker) ---\n    #     # config_module.update_config(self.model_name, 0) # Your actual config might have this\n    #     # OPENAI_API_KEY is already set by config_module.update_llm_settings\n    #     # config_module.REFERENCE_CODE_PATH = None # As in PyQt5\n    #     if hasattr(config_module, 'REFERENCE_CODE_PATH'): config_module.REFERENCE_CODE_PATH = None\n    #\n    #     # SUMMARY_JSON_PATH (参照 PyQt5 WorkflowWorker)\n    #     # Ensure the directory exists for SUMMARY_JSON_PATH_TEMPLATE\n    #     summary_json_path_formatted = config_module.SUMMARY_JSON_PATH_TEMPLATE.format(model_name=current_model_for_run)\n    #     os.makedirs(os.path.dirname(summary_json_path_formatted), exist_ok=True)\n    #     if hasattr(config_module, 'SUMMARY_JSON_PATH'):  # Check if attribute exists\n    #         config_module.SUMMARY_JSON_PATH = summary_json_path_formatted\n    #     logging.info(f\"SUMMARY_JSON_PATH set to: {summary_json_path_formatted}\")\n    #\n    #     # Call to setup_environment (PyQt5: self.setup_environment_func)\n    #     try:\n    #         # tb_path_in_exp is the path to testbench_ref.v inside the experiment directory\n    #         verilog_dir, tb_path_in_exp, ref_code_path_in_exp, summary_file_in_exp, exp_output_dir = \\\n    #             setup_environment_dummy(experiment_path, tb_src_path, exp_out_src_path, prompt)\n    #\n    #         app_state.current_log_file_path = config_module.EXPERIMENT_LOG_FILE  # Critical for log_agent_activity\n    #         logging.info(\n    #             f\"Environment setup. Log: {app_state.current_log_file_path}. Testbench in exp: {tb_path_in_exp}\")\n    #\n    #         # Update config_module with paths from setup_environment_dummy, as in PyQt5\n    #         config_module.VERILOG_DIR = verilog_dir\n    #         config_module.TESTBENCH_PATH = tb_path_in_exp  # Crucial: use path inside experiment dir\n    #         config_module.REFERENCE_CODE_PATH = ref_code_path_in_exp\n    #         config_module.SUMMARY_FILE = summary_file_in_exp\n    #\n    #         yield {\n    #             workflow_status_display: f\"Environment ready. Log: {os.path.basename(app_state.current_log_file_path)}\"}\n    #     except Exception as e:\n    #         logging.error(f\"Failed to setup environment: {e}\", exc_info=True)\n    #         yield {workflow_status_display: f\"Error setting up environment: {e}\",\n    #                design_run_button: gr.update(interactive=True)};\n    #         return\n    #     progress.update(0.15)\n    #\n    #     # --- RAG System Initialization (参照 PyQt5 WorkflowWorker) ---\n    #     try:\n    #         # These RAG-specific configs were set directly in PyQt5 WorkflowWorker, replicate if needed\n    #         # For now, assuming initialize_rag_system_dummy uses values from config_module\n    #         rag_system = initialize_rag_system_dummy(\n    #             config_module.KNOWLEDGE_BASE_PATH, config_module.DETAIL_KNOWLEDGE_BASE_PATH,\n    #             config_module.EMBEDDING_MODEL, config_module.RAG_LLM_MODEL,  # Uses workflow model\n    #             config_module.RAG_INDEX_PATH, config_module.RAG_DATA_PATH,\n    #             config_module.SUMMARY_JSON_PATH  # Uses the formatted one\n    #         )\n    #     except Exception as e:\n    #         logging.error(f\"Failed to initialize RAG system: {e}\", exc_info=True)\n    #         yield {workflow_status_display: f\"Error initializing RAG: {e}\",\n    #                design_run_button: gr.update(interactive=True)};\n    #         return\n    #     progress.update(0.2)\n    #\n    #     # --- Initialize Mediator and ALL Agents (参照 PyQt5 WorkflowWorker) ---\n    #     # These will use the global `config_module` instance, which has been updated for this run.\n    #     mediator = MediatorDummy()  # Use ActualMediator in your real code\n    #     user_proxy = AgentDummy(\"UserProxy\", mediator, config_module)  # rag_system not usually for UserProxy\n    #     coder_agent = AgentDummy(\"CoderAgent\", mediator, config_module, rag_system)\n    #     reviewer_agent = AgentDummy(\"Reviewer\", mediator, config_module, rag_system)\n    #     executor_agent = AgentDummy(\"Executor\", mediator, config_module)  # No RAG for executor\n    #     summarizer_agent = AgentDummy(\"Summarizer\", mediator, config_module)  # Summarizer might use RAG\n    #\n    #     # Helper for UI updates (log and progress)\n    #     def _update_ui_and_log(current_progress_value, status_msg=\"\"):\n    #         new_log_records, new_log_pos, log_err = app_state.log_processor.parse_log_to_chat_records(\n    #             app_state.current_log_file_path, app_state.current_log_last_pos)\n    #         if log_err: logging.error(f\"Log parsing error: {log_err}\")\n    #         if new_log_records: app_state.agent_interaction_records.extend(\n    #             new_log_records); app_state.current_log_last_pos = new_log_pos\n    #         updates = {agent_log_display_html: format_chat_records_to_html(app_state.agent_interaction_records)}\n    #         if status_msg: updates[workflow_status_display] = status_msg\n    #         progress.update(current_progress_value)\n    #         return updates\n    #\n    #     # --- Workflow Steps (mimicking implied sequence from user_proxy.submit_design_request) ---\n    #     # In PyQt5, user_proxy.submit_design_request(self.prompt) kicks off the whole chain.\n    #     # Here, we will make it more explicit for Gradio.\n    #     # Each agent \"call\" should update the mediator and log its actions.\n    #\n    #     try:\n    #         # 1. UserProxy submits request\n    #         yield _update_ui_and_log(0.25, \"UserProxy: Submitting design request...\")\n    #         user_proxy.submit_design_request(prompt)  # This dummy logs \"Received task\"\n    #\n    #         # 2. CoderAgent generates code (triggered by UserProxy or Mediator)\n    #         yield _update_ui_and_log(0.35, \"CoderAgent: Generating code...\")\n    #         # In a real system, CoderAgent might be called by UserProxy or react to a Mediator message.\n    #         # For dummy, we call its specific method.\n    #         generated_code = coder_agent.submit_design_request(\n    #             prompt)  # Reusing submit_design_request for CoderAgent's task\n    #         # Or, if CoderAgent had a specific method: generated_code = coder_agent.generate_code(prompt)\n    #         # The dummy AgentDummy's submit_design_request for CoderAgent already updates mediator.\n    #         latest_code_after_coder = mediator.get_latest_code()\n    #         final_verilog_code_output = latest_code_after_coder if latest_code_after_coder else \"CoderAgent: No code generated.\"\n    #         yield {**_update_ui_and_log(0.45), generated_verilog_display: final_verilog_code_output}\n    #\n    #         # 3. ReviewerAgent reviews code\n    #         if latest_code_after_coder:\n    #             yield _update_ui_and_log(0.50, \"ReviewerAgent: Reviewing code...\")\n    #             # review_comments = reviewer_agent.review_code(latest_code_after_coder)\n    #             reviewer_agent.submit_design_request(latest_code_after_coder)  # Dummy Reviewer task\n    #             final_verilog_code_output = mediator.get_latest_code()  # Reviewer might modify/approve\n    #             yield {**_update_ui_and_log(0.55), generated_verilog_display: final_verilog_code_output}\n    #         else:\n    #             log_agent_activity(\"SystemWorkflow\", \"Skipping Reviewer: No code from CoderAgent.\")\n    #             yield _update_ui_and_log(0.55, \"Skipping Reviewer: No code from CoderAgent.\")\n    #\n    #         # 4. ExecutorAgent executes simulation\n    #         # Executor needs the final code and the testbench path (config_module.TESTBENCH_PATH)\n    #         simulation_output_data = None\n    #         if final_verilog_code_output and config_module.TESTBENCH_PATH:\n    #             yield _update_ui_and_log(0.60, \"ExecutorAgent: Executing simulation...\")\n    #             # sim_data = executor_agent.execute_simulation(final_verilog_code_output, config_module.TESTBENCH_PATH)\n    #             executor_agent.submit_design_request(\n    #                 {\"code\": final_verilog_code_output, \"tb_path\": config_module.TESTBENCH_PATH})  # Dummy Executor task\n    #             simulation_output_data = mediator.get_latest_simulation_result()\n    #\n    #             current_sim_status_text = \"Executor: No simulation data.\"\n    #             if simulation_output_data and simulation_output_data.get(\"execution_result\"):\n    #                 actual_sim_out = simulation_output_data.get(\"execution_result\", \"\")\n    #                 sim_message = simulation_output_data.get(\"message\", \"N/A\")\n    #                 expected_sim_out_used = simulation_output_data.get(\"expected_output_used\", \"N/A\")  # From dummy\n    #                 current_sim_status_text = f\"Executor Msg: {sim_message}\\nActual Output:\\n{actual_sim_out}\\n\"\n    #                 current_sim_status_text += f\"\\nExpected Output (used by dummy sim):\\n{expected_sim_out_used}\\n\"\n    #                 match_bool, compare_msg_text = compare_simulation_results(actual_sim_out, expected_sim_out_used)\n    #                 current_sim_status_text += f\"Comparison with dummy expected: {compare_msg_text}\"\n    #             simulation_status_output = current_sim_status_text\n    #             yield {**_update_ui_and_log(0.70, simulation_status_output)}\n    #         else:\n    #             log_agent_activity(\"SystemWorkflow\", \"Skipping Executor: No code or testbench path.\")\n    #             simulation_status_output = \"Skipped simulation: No code or testbench.\"\n    #             yield _update_ui_and_log(0.70, simulation_status_output)\n    #\n    #         # 5. Analyze Verilog for Gate Metrics (after code is potentially finalized)\n    #         metrics_data_for_summary = {}\n    #         if final_verilog_code_output:\n    #             module_name_for_metrics = extract_module_name(final_verilog_code_output)\n    #             if module_name_for_metrics:\n    #                 # analyze_verilog_func is from PyQt5's self.analyze_verilog_func\n    #                 # In Gradio, this is analyze_verilog_dummy or your actual function\n    #                 metrics_data_for_summary = analyze_verilog_dummy(final_verilog_code_output, module_name_for_metrics)\n    #                 if metrics_data_for_summary.get(\"error\"):\n    #                     gate_metrics_output = f\"Error: {metrics_data_for_summary['error']}\"\n    #                 else:\n    #                     gate_metrics_output = (\n    #                         f\"Logic Gate Count: {metrics_data_for_summary.get('logic_gate_count', 'N/A')}\\n\"\n    #                         f\"Longest Delay: {metrics_data_for_summary.get('longest_delay', 'N/A')} ns\\n\"\n    #                         f\"Longest Path: {' -> '.join(metrics_data_for_summary.get('longest_path', ['N/A']))}\")\n    #             else:\n    #                 gate_metrics_output = \"Metrics: Could not extract module name.\"\n    #             yield {**_update_ui_and_log(0.75), gate_metrics_display: gate_metrics_output}\n    #         else:\n    #             gate_metrics_output = \"Metrics: No Verilog code to analyze.\"\n    #             yield {**_update_ui_and_log(0.75), gate_metrics_display: gate_metrics_output}\n    #\n    #         # 6. SummarizerAgent creates summary\n    #         yield _update_ui_and_log(0.80, simulation_status_output + \"\\nSummarizerAgent: Generating summary...\")\n    #         # summary_text = summarizer_agent.summarize(final_code, sim_results, metrics)\n    #         summarizer_agent.submit_design_request({  # Dummy Summarizer task\n    #             \"code\": final_verilog_code_output, \"sim\": simulation_output_data, \"metrics\": metrics_data_for_summary\n    #         })\n    #         # final_summary_text = mediator.get_summary() # If summarizer updates mediator\n    #         # For dummy, it just logs. We'll use the last log message as a proxy for status.\n    #         simulation_status_output += \"\\nSummary generated (see logs).\"\n    #         yield _update_ui_and_log(0.90, simulation_status_output)\n    #\n    #     except Exception as e_workflow_main:\n    #         logging.error(f\"Core workflow error: {e_workflow_main}\", exc_info=True)\n    #         log_agent_activity(\"SystemWorkflow\", f\"Core Workflow Exception: {e_workflow_main}\")\n    #         yield {**_update_ui_and_log(0.95, f\"Workflow failed: {e_workflow_main}\")}\n    #\n    #     # Final UI update\n    #     progress.update(1.0)  # 移除 desc\n    #     final_updates = _update_ui_and_log(1.0, simulation_status_output + \"\\nWorkflow complete.\")\n    #     final_updates[design_run_button] = gr.update(interactive=True)\n    #     final_updates[generated_verilog_display] = final_verilog_code_output\n    #     final_updates[gate_metrics_display] = gate_metrics_output\n    #     yield final_updates\n\n\n    # 替换你现有的 run_workflow_gradio 函数\n    def run_workflow_gradio(prompt_str, model_name_str, api_key_str):  # 完全移除 progress 参数\n        logging.info(\"--- TEST: run_workflow_gradio (ULTRA-SIMPLIFIED INPUTS, NO PROGRESS) CALLED ---\")\n        print(\"--- TEST: run_workflow_gradio (ULTRA-SIMPLIFIED INPUTS, NO PROGRESS) CALLED ---\")\n\n        logging.info(f\"Prompt received: '{prompt_str}'\")\n        logging.info(f\"Model for run: {model_name_str}\")\n        logging.info(f\"API Key for run: {'****' if api_key_str else 'None'}\")\n\n        # 由于现在不是生成器，我们直接返回一个字典用于单次更新\n        # 或者，如果为了与 Gradio 的 .click() 对生成器的期望保持一致（即使只 yield 一次）\n        # 我们可以 yield 一次。我们先试试 yield。\n\n        # 第一个 yield 会触发按钮的禁用\n        yield {\n            workflow_status_display: \"TEST: Button clicked! Processing (ultra-simple, no progress)...\",\n            design_run_button: gr.update(interactive=False)  # 确保 design_run_button 在 outputs 中\n        }\n\n        time.sleep(1)  # 模拟一些工作\n\n        logging.info(\"--- TEST: run_workflow_gradio (ULTRA-SIMPLIFIED INPUTS, NO PROGRESS) FINISHING ---\")\n        # 第二个 yield 提供最终结果并重新启用按钮\n        yield {\n            workflow_status_display: \"TEST: Ultra-simplified (no progress) processing complete!\",\n            generated_verilog_display: \"Test output (ultra-simple, no progress).\",\n            # 确保 generated_verilog_display 在 outputs 中\n            design_run_button: gr.update(interactive=True)\n        }\n        # 对于生成器，可以省略 return\n\n    # ... (rest of the Gradio UI and event handlers: chat, file previews, etc. 保持不变) ...\n    # ... (handle_chat_message, action_optimize_in_circuitmind, __main__ block 保持不变) ...\n\n    # --- chat, file previews, and __main__ block (保持不变) ---\n    def handle_chat_message(message, history, chat_model_name_from_ui, chat_api_key_from_ui):\n        if not ollama_available and not (\"gpt\" in chat_model_name_from_ui.lower()):\n            history.append((message, \"Chat LLM (Ollama) module not found.\"))\n            return history, \"\", gr.update(visible=False), gr.update(value=\"\")\n        history.append((message, None));\n        yield {chat_display: history, chat_extracted_verilog_display: \"\",\n               chat_optimize_button: gr.update(visible=False), chat_message_input: \"\"}\n        try:\n            response_content = \"\"\n            if \"gpt\" in chat_model_name_from_ui.lower() or \"openai\" in chat_model_name_from_ui.lower():\n                api_key_to_use = chat_api_key_from_ui if chat_api_key_from_ui else config_module.get_current_api_key_value(\n                    chat_model_name_from_ui)\n                if not api_key_to_use: raise ValueError(f\"API key for {chat_model_name_from_ui} not found.\")\n                logging.info(f\"Calling OpenAI-like model: {chat_model_name_from_ui}\");\n                # Actual OpenAI call placeholder\n                response_content = f\"(Simulated OpenAI for '{chat_model_name_from_ui}'): You said: {message}\"\n            elif ollama_available:\n                logging.info(f\"Calling Ollama model: {chat_model_name_from_ui}\")\n                response = ollama.chat(model=chat_model_name_from_ui, messages=[{\"role\": \"user\", \"content\": message}])\n                response_content = response['message']['content']\n            else:\n                raise ConnectionError(\"Ollama not available and model not recognized.\")\n            history[-1] = (message, response_content)\n            verilog_match = re.search(r'```verilog\\n([\\s\\S]*?)\\n```', response_content, re.MULTILINE)\n            extracted_code = verilog_match.group(1).strip() if verilog_match else \"\"\n            yield {chat_display: history, chat_extracted_verilog_display: extracted_code,\n                   chat_optimize_button: gr.update(visible=bool(extracted_code)), chat_message_input: \"\"}\n        except Exception as e:\n            logging.error(f\"Chat API error for {chat_model_name_from_ui}: {e}\", exc_info=True);\n            history[-1] = (message, f\"Error: {str(e)}\")\n            yield {chat_display: history, chat_extracted_verilog_display: \"\",\n                   chat_optimize_button: gr.update(visible=False), chat_message_input: \"\"}\n\n\n    chat_message_input.submit(handle_chat_message,\n                              inputs=[chat_message_input, chat_display, shared_model_selector, shared_api_key_input],\n                              outputs=[chat_display, chat_extracted_verilog_display, chat_optimize_button,\n                                       chat_message_input])\n    chat_send_button.click(handle_chat_message,\n                           inputs=[chat_message_input, chat_display, shared_model_selector, shared_api_key_input],\n                           outputs=[chat_display, chat_extracted_verilog_display, chat_optimize_button,\n                                    chat_message_input])\n\n\n    # def action_optimize_in_circuitmind(extracted_verilog_code):\n    #     return {design_prompt_input: extracted_verilog_code, tabs: gr.update(selected=0)}\n\n\n    def action_optimize_in_circuitmind(extracted_verilog_code):\n        logging.info(f\"Populating Design Prompt with code: {extracted_verilog_code[:50]}...\")\n        return {\n            design_prompt_input: gr.update(value=extracted_verilog_code, interactive=True),\n            tabs: gr.update(selected=0) # <-- 暂时注释掉这行\n        }\n\n    chat_optimize_button.click(action_optimize_in_circuitmind, inputs=[chat_extracted_verilog_display],\n                               outputs=[design_prompt_input, tabs])\n\nif __name__ == \"__main__\":\n    icons_dir = \"icons\";\n    os.makedirs(icons_dir, exist_ok=True)  # (Dummy icon creation logic 保持不变)\n    # ... (The rest of the dummy icon creation logic from the previous response)\n    original_get_avatar = LogProcessor.get_avatar  # Monkey patch for Gradio file serving\n\n\n    def gradio_get_avatar(self, agent_name):\n        res = original_get_avatar(self, agent_name)\n        if res[\"type\"] == \"image\" and res[\"value\"]: res[\"value\"] = os.path.abspath(res[\"value\"]).replace(os.sep, '/')\n        return res\n\n\n    LogProcessor.get_avatar = gradio_get_avatar\n    demo.queue().launch(share=False, server_port=7860,\n                        allowed_paths=[os.path.abspath(icons_dir)])\n"}}, "GUI/Gradio_ui_5.py": {"4677b755a718f5b15fc304ba3866a8e04a9a2902": {"t": 1785595589.2156258, "n": 52927, "h": "161f8cd77ce94235", "c": "import gradio as gr\nimport sys\nimport os\nimport logging\nimport re\nimport time\nimport html\nfrom datetime import datetime\nimport shutil  # For file operations in workflow\n\n# Assuming ollama is installed for the chat functionality\ntry:\n    import ollama\n\n    ollama_available = True\nexcept ImportError:\n    logging.warning(\"ollama module not found. Chat functionality will be limited.\")\n    ollama_available = False\n\n# --- Configuration and Constants (from original PyQt5 code) ---\nAGENT_AVATARS = {\n    \"UserProxy\": {\"path\": \"icons/user.png\", \"emoji\": \"😊\"},\n    \"CoderAgent\": {\"path\": \"icons/coder.png\", \"emoji\": \"💻\"},\n    \"Reviewer\": {\"path\": \"icons/reviewer.png\", \"emoji\": \"🔍\"},\n    \"Executor\": {\"path\": \"icons/executor.png\", \"emoji\": \"⚙️\"},\n    \"Summarizer\": {\"path\": \"icons/summarizer.png\", \"emoji\": \"📝\"},\n    \"root\": {\"path\": \"icons/root.png\", \"emoji\": \"🌟\"},\n    \"RAG\": {\"path\": \"icons/rag.png\", \"emoji\": \"📚\"},\n    \"Mediator\": {\"path\": \"icons/mediator.png\", \"emoji\": \"🤝\"},\n    \"ChatSession-coder\": {\"path\": \"icons/chatsession-coder.png\", \"emoji\": \"🖥️\"},\n    \"ChatSession-reviewer\": {\"path\": \"icons/chatsession-reviewer.png\", \"emoji\": \"🕵️\"},\n    \"transitions.core\": {\"path\": \"icons/transitions-core.png\", \"emoji\": \"🔄\"},\n    \"httpx\": {\"path\": \"icons/httpx.png\", \"emoji\": \"🌐\"},\n    \"default\": {\"path\": \"icons/default.png\", \"emoji\": \"❓\"}\n}\n\n# --- Logging Setup ---\nlogging.basicConfig(stream=sys.stdout, level=logging.INFO,\n                    # Changed to INFO for Gradio, DEBUG is too verbose for typical use\n                    format='[%(asctime)s] [%(levelname)s] [%(name)s:%(lineno)d] %(message)s')\n\n\n# --- Utility Functions (from original PyQt5 code or adapted) ---\nclass LogProcessor:\n    def __init__(self, log_format_regex, agent_format_regex, timestamp_format_str=None,\n                 avatar_dir=\"avatars_gradio/resized\"):\n        self.log_format = re.compile(log_format_regex, re.MULTILINE)\n        self.agent_format = re.compile(agent_format_regex)  # This regex might be too specific\n        self.timestamp_format = timestamp_format_str\n        self.avatar_dir = avatar_dir\n        os.makedirs(self.avatar_dir, exist_ok=True)\n        self.color_code_regex = re.compile(r'\\x1B\\[[0-9;]*[mGKHfJABCD]?')\n        self.current_conversation = None\n        self.current_agent = None\n        self.current_timestamp = None\n        self.current_log_level = None\n        self.message_buffer = []\n        self.avatar_cache = {}  # For resized avatars\n\n    def remove_color_codes(self, text):\n        return self.color_code_regex.sub('', text)\n\n    def clean_message(self, message):\n        message = self.remove_color_codes(message)\n        message = message.strip()\n        return message if message else None\n\n    def get_avatar(self, agent_name):\n        avatar_info = AGENT_AVATARS.get(agent_name, AGENT_AVATARS[\"default\"])\n        avatar_path = avatar_info[\"path\"]\n        emoji = avatar_info[\"emoji\"]\n\n        # In Gradio, ensure paths are web-accessible or use emojis.\n        # For local image files, they need to be in a directory Gradio can serve\n        # or use absolute paths if your environment allows.\n        # For simplicity, we'll primarily rely on emoji if path doesn't resolve easily,\n        # or assume `avatar_path` can be made web-accessible (e.g. by placing `icons` in cwd).\n        if os.path.exists(avatar_path):  # Check if original icon exists\n            # Resizing logic (simplified for Gradio, as direct QPixmap not available)\n            # We'll assume icons are already reasonably sized or use original if accessible.\n            # The HTML will refer to the original path for now.\n            return {\"type\": \"image\", \"value\": avatar_path, \"emoji\": emoji}\n        return {\"type\": \"emoji\", \"value\": emoji}\n\n    def flush_buffer(self, chat_records):\n        if not self.message_buffer or not self.current_agent:\n            self.message_buffer = []\n            return\n\n        message = '\\n'.join(self.message_buffer)\n        cleaned_message = self.clean_message(message)\n        if cleaned_message:\n            formatted_time = self.current_timestamp or datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n            try:\n                if self.current_timestamp and self.timestamp_format:\n                    time_obj = datetime.strptime(self.current_timestamp, self.timestamp_format)\n                    formatted_time = time_obj.strftime('%Y-%m-%d %H:%M:%S')\n            except (ValueError, TypeError) as e:\n                logging.warning(f\"Timestamp parsing failed ({e}), using raw: {self.current_timestamp}\")\n\n            avatar = self.get_avatar(self.current_agent)\n            record = {\n                'time': formatted_time,\n                'agent': self.current_agent,\n                'avatar': avatar,\n                'message': cleaned_message\n            }\n            chat_records.append(record)\n        self.message_buffer = []\n\n    def parse_log_to_chat_records(self, log_file_path, last_position):\n        chat_records = []\n        error_message = None\n        new_position = last_position\n\n        if not log_file_path or not os.path.exists(log_file_path):\n            return chat_records, new_position, \"Log file does not exist.\"\n        if os.path.getsize(log_file_path) == 0:\n            return chat_records, new_position, \"Log file is empty.\"\n\n        try:\n            with open(log_file_path, 'r', encoding='utf-8') as f:\n                f.seek(last_position)\n                content = f.read()\n                new_position = f.tell()\n\n            if not content:\n                return chat_records, new_position, None  # No new content\n\n            cleaned_content = self.remove_color_codes(content)  # Clean once\n            lines = cleaned_content.splitlines()\n\n        except Exception as e:\n            return chat_records, last_position, f\"Error reading log file: {str(e)}\"\n\n        for line in lines:\n            line = line.strip()\n            if not line:\n                continue\n\n            match = self.log_format.match(line)\n            if match:\n                self.flush_buffer(chat_records)  # Flush previous buffered message\n                log_data = match.groupdict()\n                timestamp = log_data.get('timestamp', '')\n                log_level = log_data.get('log_level', '').upper()\n                agent_name = log_data.get('agent_name', '')\n                message_part = log_data.get('message', '').strip()\n\n                # Filter for relevant agents/levels if necessary (original code had a filter)\n                if log_level not in ['DIALOGUE', 'INFO', 'DEBUG'] or not AGENT_AVATARS.get(agent_name):\n                    # If it's not a recognized agent log line, but we had a current agent,\n                    # append this line to its buffer as continuation.\n                    if self.current_agent and message_part:  # Add current line as part of previous message\n                        self.message_buffer.append(line)  # Append the raw line for context\n                    continue\n\n                self.current_agent = agent_name\n                self.current_timestamp = timestamp\n                self.current_log_level = log_level\n                if message_part:\n                    self.message_buffer.append(message_part)\n\n            elif self.current_agent:  # Line does not match main log format, but we have an active agent\n                # This part handles multi-line messages\n                self.message_buffer.append(line)\n            # else:\n            # Lines that don't match and no current agent are ignored or handled by agent_format\n            # The agent_format regex was: r'^\\s*, agent=(?P<agent_name>[^\\s,]+)(?:, message=(?P<message>.*))?$'\n            # This seems intended for a very specific format, might not be common.\n            # For simplicity in Gradio, we primarily rely on the main log_format.\n            # If agent_format is critical, its handling needs to be integrated here.\n\n        self.flush_buffer(chat_records)  # Flush any remaining messages\n        return chat_records, new_position, error_message\n\n\ndef format_chat_records_to_html(chat_records_list):\n    if not chat_records_list:\n        return \"<p>No agent interactions logged yet.</p>\"\n\n    html_parts = [\n        \"<style>\",\n        \"body { font-family: Arial, sans-serif; margin: 0px; }\",\n        \".message-container { margin-bottom: 12px; display: flex; align-items: flex-start; padding: 5px;}\",\n        \".avatar-container { width: 32px; height: 32px; margin-right: 8px; flex-shrink: 0; display:flex; align-items:center; justify-content:center; }\",\n        \".avatar-img { width: 30px; height: 30px; border-radius: 50%; }\",\n        \".avatar-emoji { font-size: 24px; }\",\n        \".content-container { display: flex; flex-direction: column; max-width: calc(100% - 50px); }\",\n        \".header { display: flex; align-items: center; margin-bottom: 3px; }\",\n        \".agent-name { font-weight: bold; margin-right: 8px; font-size: 0.9em; color: #333; }\",\n        \".timestamp { color: #777; font-size: 0.8em; }\",\n        \".bubble { background-color: #f0f0f0; border-radius: 8px; padding: 8px 10px; font-size: 0.95em; color: #333; white-space: pre-wrap; word-wrap: break-word; box-shadow: 0 1px 1px rgba(0,0,0,0.05); }\",\n        \"</style>\",\n        \"<div>\"  # Main container for all messages\n    ]\n\n    for record in chat_records_list:\n        escaped_message = html.escape(record['message'])\n        agent_name = html.escape(record['agent'])\n        timestamp = html.escape(record['time'])\n        avatar_data = record.get('avatar', {})\n\n        html_parts.append('<div class=\"message-container\">')\n        html_parts.append('<div class=\"avatar-container\">')\n        if avatar_data.get('type') == 'image' and avatar_data.get('value') and os.path.exists(avatar_data['value']):\n            # Serve icons by placing 'icons' directory in the same place as this script,\n            # or use absolute paths if necessary and security allows.\n            # For Gradio, it's often best if static files are served by Gradio or a web server.\n            # If `avatar_data['value']` is like \"icons/user.png\", it should work if 'icons' is in cwd.\n            # Gradio can serve files if an `allowed_paths` is set or using `gr.File(value=\"path/to/img.png\")`\n            # For now, assume the path is resolvable by the browser if Gradio runs locally.\n            # A more robust way is to copy icons to a known served path or use base64, but that adds complexity.\n            avatar_path_web = avatar_data['value']  # Pass through; ensure this path is web accessible\n            html_parts.append(f'<img src=\"{avatar_path_web}\" class=\"avatar-img\" alt=\"{agent_name}\">')\n        else:\n            emoji_val = avatar_data.get('value', AGENT_AVATARS[\"default\"][\"emoji\"])\n            html_parts.append(f'<span class.avatar-emoji\">{emoji_val}</span>')\n        html_parts.append('</div>')  # end avatar-container\n\n        html_parts.append('<div class=\"content-container\">')\n        html_parts.append('<div class=\"header\">')\n        html_parts.append(f'<span class=\"agent-name\">{agent_name}</span>')\n        html_parts.append(f'<span class=\"timestamp\">[{timestamp}]</span>')\n        html_parts.append('</div>')  # end header\n        html_parts.append(f'<div class=\"bubble\">{escaped_message}</div>')\n        html_parts.append('</div>')  # end content-container\n        html_parts.append('</div>')  # end message-container\n\n    html_parts.append(\"</div>\")\n    return \"\".join(html_parts)\n\n\ndef compare_simulation_results(actual_output, expected_output):\n    actual_lines = actual_output.strip().split('\\n')\n    actual_results = []\n    for line in actual_lines:\n        match = re.match(r\"Time=\\d+:\\s*in=(\\d),\\s*out=(\\d)\", line.strip())\n        if match:\n            actual_results.append((match.group(1), match.group(2)))\n\n    expected_lines = expected_output.strip().split('\\n')\n    expected_results = []\n    for line in expected_lines:\n        match = re.match(r\"Time=\\d+:\\s*in=(\\d),\\s*out=(\\d)\", line.strip())\n        if match:\n            expected_results.append((match.group(1), match.group(2)))\n\n    if not actual_results and not expected_results and actual_output.strip() == expected_output.strip():\n        return True, \"Outputs match (non-structured).\"\n    if not actual_results or not expected_results:\n        return False, f\"Output format mismatch or empty. Actual: {len(actual_results)} structured lines, Expected: {len(expected_results)} structured lines.\"\n\n    if len(actual_results) != len(expected_results):\n        return False, f\"Output length mismatch: expected {len(expected_results)} lines, got {len(actual_results)}\"\n\n    for i, (actual, expected) in enumerate(zip(actual_results, expected_results)):\n        if actual != expected:\n            return False, f\"Mismatch at line {i + 1}: expected in={expected[0]}, out={expected[1]}; got in={actual[0]}, out={actual[1]}\"\n\n    return True, \"All structured tests passed.\"\n\n\ndef extract_module_name(verilog_code):\n    if not verilog_code: return None\n    match = re.search(r'module\\s+(\\w+)\\s*\\(', verilog_code)\n    return match.group(1) if match else None\n\n\n# --- Mock/Placeholder Backend Modules & Classes (replace with your actual imports) ---\n# These are simplified versions based on the PyQt5 WorkflowWorker's imports\nclass MockConfigModule:\n    EXPERIMENTS_ROOT = \"./experiments_gradio\"  # Ensure this directory exists or is created\n    OPENAI_API_KEY = \"\"  # Will be set from UI\n    # Define other paths as expected by your backend logic\n    KNOWLEDGE_BASE_PATH = './knowledge_base/RAG-data'\n    DETAIL_KNOWLEDGE_BASE_PATH = './knowledge_base/RAG-data-detail'\n    EMBEDDING_MODEL = 'nomic-embed-text'\n    RAG_LLM_MODEL = 'qwen2.5-coder:14b'  # Default, can be changed by user\n    RAG_INDEX_PATH = './knowledge_base/RAG-data/vector_index.faiss'\n    RAG_DATA_PATH = './knowledge_base/RAG-data/vector_data.json'\n    SUMMARY_JSON_PATH_TEMPLATE = \"./knowledge_base/Model-incrementment/{model_name}.json\"\n\n    # These will be dynamically set during workflow\n    VERILOG_DIR = \"\"\n    TESTBENCH_PATH = \"\"\n    REFERENCE_CODE_PATH = None\n    SUMMARY_FILE = \"\"\n    EXPERIMENT_LOG_FILE = \"\"  # Path to the current experiment's log file\n\n    # Model configs from PyQt version\n    api_configs = {\n        \"glm-4-plus\": {\n            \"API_KEY\": [\"780fce9dabb5a1005e38baf6b6e556d8.VCLi2kOdjLs6DQtM\"],\n            \"BASE_URL\": \"https://open.bigmodel.cn/api/paas/v4/\",\n        },\n        \"deepseek-chat\": {\n            \"API_KEY\": [\"sk-f73a42bc3f1643b69059757101005d0d\"],\n            \"BASE_URL\": \"https://api.deepseek.com\",\n        },\n        \"gpt-4o\": {\n            \"API_KEY\": [\n                \"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\"],\n            \"BASE_URL\": \"https://api.openai.com/v1\",\n        },\n        \"gpt-4o-mini\": {\n            \"API_KEY\": [\n                \"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\"],\n            \"BASE_URL\": \"https://api.openai.com/v1\",\n        },\n        \"glm-4-air\": {\n            \"API_KEY\": [\"780fce9dabb5a1005e38baf6b6e556d8.VCLi2kOdjLs6DQtM\"],\n            \"BASE_URL\": \"https://open.bigmodel.cn/api/paas/v4/\",\n        },\n        \"qwq:latest\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.148.206:11434/v1\",\n        },\n        \"qwq:32b\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.149.18:11434/v1\",\n        },\n\n        \"qwq:32b-q8_0\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.144.196:11434/v1\",\n        },\n        \"qwen2.5-coder:14b\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.149.18:11434/v1\",\n        },\n        \"qwen2.5:14b\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.144.196:11434/v1\",\n        },\n        \"deepseek-coder:33b\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.148.206:11434/v1\",\n        },\n        \"deepseek-coder:6.7b\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.149.18:11436/v1\",\n        },\n        \"qwen2.5:7b\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://localhost:11434/v1\",\n        },\n        \"qwen2.5:7b-instruct-fp16\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.148.206:11434/v1\",\n        },\n        \"qwen2.5-coder:32b\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.148.206:11436/v1\",\n        },\n        \"deepseek-ai/DeepSeek-V3\": {\n            \"API_KEY\": [\"sk-ptyrqcoakodsijdqglxbzucezfvdmdolzxxfafdpozixljdr\"],\n            \"BASE_URL\": \"https://api.siliconflow.cn/v1\"\n        },\n        \"deepseek-r1-250120\": {\n            \"API_KEY\": [\"009039c2-755b-4abd-8d76-225b3456ea29\", \"0a12f045-e34a-46b2-b3a8-999ff9342fcc\",\n                        \"137d25a5-b809-4a3c-a2ec-22d984d653df\", \"4ac99dcf-cf55-4e56-b25c-879d80e59e2c\", ],\n            \"BASE_URL\": \"https://ark.cn-beijing.volces.com/api/v3/\"\n        },\n        \"deepseek-r1\": {\n            \"API_KEY\": [\"sk-da2iftmgizerrfjj\"],\n            \"BASE_URL\": \"https://cloud.infini-ai.com/maas/v1\"\n        },\n        \"deepseek-r1-distill-qwen-32b\": {\n            \"API_KEY\": [\"sk-da2jurhjtunkwnuz\"],\n            \"BASE_URL\": \"https://cloud.infini-ai.com/maas/v1\"\n        },\n        \"qwq-32b\": {\n            \"API_KEY\": [\"sk-da2juiy2cochoavl\"],\n            \"BASE_URL\": \"https://cloud.infini-ai.com/maas/v1\"\n        },\n        \"deepseek-v3\": {\n            \"API_KEY\": [\"sk-da2iftmgizerrfjj\"],\n            \"BASE_URL\": \"https://cloud.infini-ai.com/maas/v1\"\n        },\n        \"qwen2.5-coder-32b-instruct\": {\n            \"API_KEY\": [\"sk-da2jurhjtunkwnuz\"],\n            \"BASE_URL\": \"https://cloud.infini-ai.com/maas/v1\"\n        },\n        \"deepseek-v3-250324\": {\n            \"API_KEY\": [\"e99646c6-b52a-45a2-ad49-c17415174d8d\"],\n            \"BASE_URL\": \"https://ark.cn-beijing.volces.com/api/v3/\"\n        },\n        \"deepseek-r1:70b\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.144.196:11434/v1\",\n        },\n        \"phi4:latest\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.144.196:11434/v1\",\n        },\n        \"deepseek-r1:32b\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.144.208:11437/v1\",\n        },\n        \"deepseek-r1:14b\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.144.208:11436/v1\",\n        },\n\n        \"gemini-2.0-flash\": {\n            \"API_KEY\": [\"AIzaSyB0PscblS6t4TSreKYY3uFPMHJb1nwA4b0\", \"AIzaSyAWmeLGAx9RctoqbLlaHt__g747P3Bdg3s\", ],\n            \"BASE_URL\": \"https://generativelanguage.googleapis.com/v1beta/\"\n        },\n        \"gemini-2.0-flash-thinking-exp\": {\n            \"API_KEY\": [\n                \"AIzaSyCe1FCm51LdQGv_lQalEeYwz7OsEfwauwg\",\n                \"AIzaSyCsdInwWPMIFMJ3vQJfiTKRZPnsRmLZgJY\",\n                \"AIzaSyCVpmMEKNuRJRqVzCWmVfUKfFnbKZitYxI\",\n                \"AIzaSyDp_C9d7fxlPw2n71vokC7zbV1AIOsp-zM\",\n                \"AIzaSyCATsoE4Luq3AtoZIUbzyPZVFHPeWEn1QU\",\n                \"AIzaSyBO13wFHgRgDtdw_vKtX853q_zMdmTBPV8\",\n                \"AIzaSyBhdQ6MRWk5wkxFKOrABAXDFaBGVWIzOXs\",\n                \"AIzaSyAWmeLGAx9RctoqbLlaHt__g747P3Bdg3s\",\n                \"AIzaSyD5xok4XZST4m4kaxRZPxtyrBFaFgTN7jM\",\n                \"AIzaSyCYfvOUl8sCo8spAtq4Oe20ZFIiCJyHtY8\",\n                \"AIzaSyDqQNRPlppxGW_m6bSS6lcPdAZzWZpbEGQ\",\n                \"AIzaSyCRMMkCtRnAFj5E3X5H1lnIq5HeJXKhqpw\",\n                \"AIzaSyCvs-dZ-oO-T3NAYMAC2PzEbmUz824WJds\",\n                \"AIzaSyB2NYBW-cQ8KPE_ksMO5qZRFHRJ8GOXpoE\",\n                \"AIzaSyCogr95x1PyNeHNuG-WK7Bkw9piSyRbMEY\",\n                \"AIzaSyCxxCys9WVW3FuPgRWzomXOhGEqgPTenDA\",\n            ],\n            \"BASE_URL\": \"https://generativelanguage.googleapis.com/v1beta/\"\n        },\n\n        \"gemini-2.0-pro-exp\": {\n            \"API_KEY\": [\n                \"AIzaSyCe1FCm51LdQGv_lQalEeYwz7OsEfwauwg\",\n                \"AIzaSyCxxCys9WVW3FuPgRWzomXOhGEqgPTenDA\",\n                \"AIzaSyCsdInwWPMIFMJ3vQJfiTKRZPnsRmLZgJY\",\n                \"AIzaSyCVpmMEKNuRJRqVzCWmVfUKfFnbKZitYxI\",\n                \"AIzaSyDp_C9d7fxlPw2n71vokC7zbV1AIOsp-zM\",\n                \"AIzaSyCATsoE4Luq3AtoZIUbzyPZVFHPeWEn1QU\",\n                \"AIzaSyBO13wFHgRgDtdw_vKtX853q_zMdmTBPV8\",\n                \"AIzaSyBhdQ6MRWk5wkxFKOrABAXDFaBGVWIzOXs\",\n                \"AIzaSyAWmeLGAx9RctoqbLlaHt__g747P3Bdg3s\",\n                \"AIzaSyD5xok4XZST4m4kaxRZPxtyrBFaFgTN7jM\",\n                \"AIzaSyCYfvOUl8sCo8spAtq4Oe20ZFIiCJyHtY8\",\n                \"AIzaSyDqQNRPlppxGW_m6bSS6lcPdAZzWZpbEGQ\",\n                \"AIzaSyCRMMkCtRnAFj5E3X5H1lnIq5HeJXKhqpw\",\n                \"AIzaSyCvs-dZ-oO-T3NAYMAC2PzEbmUz824WJds\",\n                \"AIzaSyB2NYBW-cQ8KPE_ksMO5qZRFHRJ8GOXpoE\",\n                \"AIzaSyCogr95x1PyNeHNuG-WK7Bkw9piSyRbMEY\",\n            ],\n            \"BASE_URL\": \"https://generativelanguage.googleapis.com/v1beta/\"\n        },\n        \"gemini-2.0-flash-exp\": {\n\n            \"API_KEY\": [\n                \"AIzaSyCsdInwWPMIFMJ3vQJfiTKRZPnsRmLZgJY\",\n                \"AIzaSyCVpmMEKNuRJRqVzCWmVfUKfFnbKZitYxI\",\n                \"AIzaSyDp_C9d7fxlPw2n71vokC7zbV1AIOsp-zM\",\n                \"AIzaSyCATsoE4Luq3AtoZIUbzyPZVFHPeWEn1QU\",\n                \"AIzaSyBO13wFHgRgDtdw_vKtX853q_zMdmTBPV8\",\n                \"AIzaSyBhdQ6MRWk5wkxFKOrABAXDFaBGVWIzOXs\",\n                \"AIzaSyAWmeLGAx9RctoqbLlaHt__g747P3Bdg3s\",\n                \"AIzaSyD5xok4XZST4m4kaxRZPxtyrBFaFgTN7jM\",\n                \"AIzaSyCYfvOUl8sCo8spAtq4Oe20ZFIiCJyHtY8\",\n                \"AIzaSyDqQNRPlppxGW_m6bSS6lcPdAZzWZpbEGQ\",\n                \"AIzaSyCRMMkCtRnAFj5E3X5H1lnIq5HeJXKhqpw\",\n                \"AIzaSyCvs-dZ-oO-T3NAYMAC2PzEbmUz824WJds\",\n                \"AIzaSyB2NYBW-cQ8KPE_ksMO5qZRFHRJ8GOXpoE\",\n                \"AIzaSyCogr95x1PyNeHNuG-WK7Bkw9piSyRbMEY\",\n                \"AIzaSyCe1FCm51LdQGv_lQalEeYwz7OsEfwauwg\",\n                \"AIzaSyCxxCys9WVW3FuPgRWzomXOhGEqgPTenDA\",\n            ],\n\n            \"BASE_URL\": \"https://generativelanguage.googleapis.com/v1beta/\"\n        },\n        \"grok-3-latest\": {\n            \"API_KEY\": [\"xai-PRXLUHFvDdVPqNunuWOp2aGrG6h520hR3xXq5WiriowRlONwtbkC6bN3VcCDRw2ROBj8M23IwaQBMCiS\"],\n            \"BASE_URL\": \"https://api.x.ai/v1\"\n        },\n        \"RV-Qwen3-S1\": {\n            \"API_KEY\": [\"ollama\"],\n            \"BASE_URL\": \"http://10.130.144.196:11434/v1\",\n        }\n\n    }\n    default_model = \"qwen2.5-coder:14b\"\n\n    def __init__(self):\n        os.makedirs(self.EXPERIMENTS_ROOT, exist_ok=True)\n        # Create knowledge base dirs if they don't exist to prevent errors in dummy setup\n        os.makedirs(os.path.dirname(self.SUMMARY_JSON_PATH_TEMPLATE), exist_ok=True)\n        os.makedirs(self.KNOWLEDGE_BASE_PATH, exist_ok=True)\n\n    def get_current_api_key(self, model_name_str):\n        return self.api_configs.get(model_name_str, {}).get(\"api_key\", \"\")\n\n    def update_llm_settings(self, model_name, api_key_override):\n        self.RAG_LLM_MODEL = model_name  # Or whatever config field your backend uses for the main LLM\n        if api_key_override:\n            self.OPENAI_API_KEY = api_key_override\n        else:\n            self.OPENAI_API_KEY = self.get_current_api_key(model_name)\n        logging.info(\n            f\"Config updated: Model={model_name}, API Key Used: {'Override' if api_key_override else 'Default/Configured'}\")\n\n\nconfig_module = MockConfigModule()\n\n\n# --- Dummy backend functions/classes (replace with your actual logic) ---\ndef setup_environment_dummy(experiment_path, testbench_src_path, expected_out_src_path, prompt_content):\n    logging.info(f\"Setting up dummy environment in: {experiment_path}\")\n    os.makedirs(experiment_path, exist_ok=True)\n\n    verilog_dir = os.path.join(experiment_path, \"generated_verilog\")\n    os.makedirs(verilog_dir, exist_ok=True)\n    config_module.VERILOG_DIR = verilog_dir\n\n    # Copy testbench and expected output to experiment directory\n    # The names \"testbench_ref.v\" and \"expected_output.txt\" are examples.\n    # Your backend might expect specific names.\n    testbench_dest_path = os.path.join(experiment_path, \"testbench_ref.v\")\n    if testbench_src_path and os.path.exists(testbench_src_path):\n        shutil.copy(testbench_src_path, testbench_dest_path)\n        config_module.TESTBENCH_PATH = testbench_dest_path\n    else:\n        logging.warning(\"Testbench source path not provided or not found.\")\n        # Create a dummy if not provided, so backend doesn't fail on missing file\n        with open(testbench_dest_path, \"w\") as f:\n            f.write(\"// Dummy testbench from setup\")\n        config_module.TESTBENCH_PATH = testbench_dest_path\n\n    if expected_out_src_path and os.path.exists(expected_out_src_path):\n        shutil.copy(expected_out_src_path, os.path.join(experiment_path, \"expected_output.txt\"))  # Example name\n\n    # Write prompt to a file, as in original code\n    prompt_file_path = os.path.join(experiment_path, \"prompt.txt\")\n    with open(prompt_file_path, \"w\", encoding=\"utf-8\") as f:\n        f.write(prompt_content)\n\n    summary_file_path = os.path.join(experiment_path, \"summary.json\")  # Example\n    config_module.SUMMARY_FILE = summary_file_path\n\n    experiment_log_file = os.path.join(experiment_path, \"experiment_run.log\")\n    config_module.EXPERIMENT_LOG_FILE = experiment_log_file\n    # Touch the log file\n    with open(experiment_log_file, \"w\") as f:\n        f.write(\n            f\"[{datetime.now().strftime('%Y-%m-%d %H:%M:%S,%f')[:-3]}] [INFO] [System] Log initialized for experiment.\\n\")\n\n    # Return structure: verilog_dir, testbench_path_in_exp, ref_code_path, summary_file_path_in_exp, experiment_output_dir (can be experiment_path)\n    return (verilog_dir, testbench_dest_path, None, summary_file_path, experiment_path)\n\n\ndef initialize_rag_system_dummy(*args):\n    logging.info(\"Initializing dummy RAG system.\")\n    return \"rag_system_instance\"\n\n\ndef analyze_verilog_dummy(verilog_code, module_name, model=\"QWQ\"):  # model param from original\n    logging.info(f\"Analyzing Verilog for module: {module_name} (dummy)\")\n    if not verilog_code or not module_name:\n        return {\"error\": \"No code or module name for analysis.\"}\n    # Simulate some metrics\n    num_lines = len(verilog_code.splitlines())\n    return {\n        \"logic_gate_count\": num_lines * 2 + 5,  # Dummy calculation\n        \"longest_delay\": round(num_lines * 0.3 + 1.2, 2),\n        \"longest_path\": [f\"path_seg1_{module_name}\", \"internal_seg\", f\"out_seg_{num_lines}\"]\n    }\n\n\nclass MediatorDummy:\n    def __init__(self):\n        self._latest_code = None\n        self._latest_sim_result = None\n        logging.info(\"MediatorDummy initialized.\")\n\n    def get_latest_code(self, agent_name=\"\", message=\"\"):  # Params from original\n        return self._latest_code\n\n    def get_latest_simulation_result(self, agent_name=\"\", message=\"\"):\n        return self._latest_sim_result\n\n    def set_code(self, code, agent=\"CoderAgent\"):  # Helper for dummy\n        self._latest_code = code\n        log_agent_activity(agent, f\"Submitted code:\\n{code[:100]}...\")\n\n    def set_simulation_result(self, result, agent=\"Executor\"):  # Helper for dummy\n        self._latest_sim_result = result\n        log_agent_activity(agent, f\"Submitted simulation result: {result.get('message', 'No message')}\")\n\n\nclass AgentDummy:\n    def __init__(self, name, mediator, config, rag_system=None):\n        self.name = name\n        self.mediator = mediator\n        self.config = config  # This should be our global config_module\n        self.rag_system = rag_system\n        logging.info(f\"AgentDummy '{name}' initialized.\")\n        log_agent_activity(self.name, \"Initialized.\")\n\n    def submit_design_request(self, prompt):\n        log_agent_activity(self.name, f\"Received design request: '{prompt[:50]}...'\")\n        if self.name == \"UserProxy\":  # Simulate the workflow triggering other agents\n            # Simulate CoderAgent\n            log_agent_activity(\"CoderAgent\", \"Starting code generation process...\")\n            time.sleep(0.5)  # Simulate work\n            generated_code = f\"module {extract_module_name(prompt) or 'top_module'} (\\n  // ... inputs and outputs ...\\n);\\n  // Verilog code based on: {prompt}\\n  // Generated by CoderAgent\\nendmodule\"\n            self.mediator.set_code(generated_code, agent=\"CoderAgent\")\n            log_agent_activity(\"CoderAgent\", f\"Generated Verilog code. Size: {len(generated_code)} bytes.\")\n\n            # Simulate Reviewer\n            log_agent_activity(\"Reviewer\", \"Starting code review...\")\n            time.sleep(0.3)\n            log_agent_activity(\"Reviewer\", \"Code review: LGTM! (Looks Good To Me)\")\n\n            # Simulate Executor\n            log_agent_activity(\"Executor\", \"Preparing for simulation...\")\n            time.sleep(0.5)\n            # Try to read expected output from the environment if available\n            expected_output_content = \"Time=0: in=0, out=1\\nTime=10: in=1, out=0\"  # Default\n            exp_out_path = os.path.join(os.path.dirname(self.config.TESTBENCH_PATH), \"expected_output.txt\")\n            if os.path.exists(exp_out_path):\n                with open(exp_out_path, 'r') as f:\n                    expected_output_content = f.read().strip()\n\n            # Simulate a simple match or mismatch based on prompt\n            sim_passed = \"simple\" in prompt.lower()  # Dummy condition for pass/fail\n            actual_sim_output = expected_output_content if sim_passed else \"Time=0: in=0, out=0\\nTime=10: in=1, out=1\"\n\n            sim_result_msg = \"All tests passed.\" if sim_passed else \"Simulation failed: Output mismatch.\"\n            self.mediator.set_simulation_result({\n                \"execution_result\": actual_sim_output,\n                \"message\": sim_result_msg\n            }, agent=\"Executor\")\n            log_agent_activity(\"Executor\", f\"Simulation finished. Result: {sim_result_msg}\")\n\n            # Simulate Summarizer\n            log_agent_activity(\"Summarizer\", \"Generating summary of the process...\")\n            time.sleep(0.2)\n            log_agent_activity(\"Summarizer\", \"Workflow concluded. Results available.\")\n\n\n# --- Gradio Application State and Helper ---\nclass GradioAppState:\n    def __init__(self):\n        self.current_log_file_path = None  # Path for current workflow log\n        self.current_log_last_pos = 0\n        self.log_processor = LogProcessor(\n            r'^\\[(?P<timestamp>\\d{4}-\\d{2}-\\d{2} \\d{2}:\\d{2}:\\d{2},\\d{3})\\] \\[(?P<log_level>.*?)\\] \\[(?P<agent_name>.*?)\\]\\s*(?P<message>.*)',\n            r'^\\s*, agent=(?P<agent_name>[^\\s,]+)(?:, message=(?P<message>.*))?$',  # Original agent_format regex\n            '%Y-%m-%d %H:%M:%S,%f'\n        )\n        self.agent_interaction_records = []  # Store records for current display\n        self.testbench_file_path = None  # Temp path from gr.File\n        self.expected_output_file_path = None  # Temp path from gr.File\n        self.active_model_name = config_module.default_model\n        self.active_api_key = config_module.get_current_api_key(config_module.default_model)\n\n\napp_state = GradioAppState()\n\n\n# Helper to write to the designated experiment log file\ndef log_agent_activity(agent_name, message):\n    if app_state.current_log_file_path and os.path.exists(os.path.dirname(app_state.current_log_file_path)):\n        timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S,%f')[:-3]\n        # Using a log level like DIALOGUE as in original parsing logic\n        log_entry = f\"[{timestamp}] [DIALOGUE] [{agent_name}] {message}\\n\"\n        try:\n            with open(app_state.current_log_file_path, \"a\", encoding=\"utf-8\") as f:\n                f.write(log_entry)\n        except Exception as e:\n            logging.error(f\"Failed to write agent activity to log {app_state.current_log_file_path}: {e}\")\n    else:\n        logging.info(f\"Log (no file): [{agent_name}] {message}\")\n\n\n# --- Gradio UI Definition ---\nwith gr.Blocks(theme=gr.themes.Soft(), title=\"VeriOptima (Gradio)\") as demo:\n    gr.Markdown(\"# VeriOptima: Verilog Optimization & Chat Agent\")\n\n    # Shared model and API key components (could be per-tab if desired)\n    with gr.Row(equal_height=False):\n        shared_model_selector = gr.Dropdown(\n            label=\"Select LLM Model (Global)\",\n            choices=list(config_module.api_configs.keys()),\n            value=app_state.active_model_name,\n            interactive=True\n        )\n        shared_api_key_input = gr.Textbox(\n            label=\"API Key (Global, overrides default if set)\",\n            placeholder=\"Enter API key for selected model\",\n            value=app_state.active_api_key,  # Show default/current\n            type=\"password\",\n            interactive=True\n        )\n\n    gr.HTML(\"<hr>\")  # Visual separator\n\n    with gr.Tabs() as tabs:\n        with gr.TabItem(\"CircuitMind (Design Optimization)\", id=0):\n            with gr.Row():\n                with gr.Column(scale=2):\n                    design_prompt_input = gr.Textbox(lines=5, label=\"Design Requirements / Verilog to Optimize\",\n                                                     placeholder=\"Enter design prompt or paste Verilog code here...\")\n                    with gr.Row():\n                        design_testbench_file = gr.File(label=\"Upload Testbench File (.v)\")\n                        design_expected_output_file = gr.File(label=\"Upload Expected Output File (.txt)\")\n                    with gr.Row():\n                        design_testbench_preview = gr.Textbox(label=\"Testbench Preview\", lines=8, interactive=False,\n                                                              max_lines=10)\n                        design_expected_output_preview = gr.Textbox(label=\"Expected Output Preview\", lines=8,\n                                                                    interactive=False, max_lines=10)\n                    design_run_button = gr.Button(\"Run Optimization Workflow\", variant=\"primary\")\n                with gr.Column(scale=1):\n                    generated_verilog_display = gr.Code(label=\"Generated/Optimized Verilog Code\",\n                                                        interactive=False)\n                    gate_metrics_display = gr.Textbox(label=\"Gate Metrics & Analysis\", lines=5, interactive=False)\n                    workflow_status_display = gr.Textbox(label=\"Workflow Status & Main Results\", lines=5,\n                                                         interactive=False)\n\n            gr.Markdown(\"### Agent Interactions Log\")\n            agent_log_display_html = gr.HTML(value=\"<p>Agent interactions will appear here...</p>\")\n\n        with gr.TabItem(\"ReasoningV (Chat with LLM)\", id=1):\n            chat_display = gr.Chatbot(label=\"Chat with ReasoningV\", height=500)\n            chat_message_input = gr.Textbox(label=\"Your Message\", placeholder=\"Type your message and press Enter...\")\n            chat_send_button = gr.Button(\"Send\", variant=\"primary\")\n\n            gr.Markdown(\"### Extracted Verilog from Chat\")\n            chat_extracted_verilog_display = gr.Code(label=\"Verilog Code from Conversation\",\n                                                     interactive=False)\n            chat_optimize_button = gr.Button(\"Optimize this Verilog in CircuitMind\", visible=False)\n\n\n    # --- Event Handlers & Backend Logic ---\n\n    # Shared Model/API Key Update Logic\n    def update_active_llm_settings(model_name, api_key):\n        app_state.active_model_name = model_name\n        app_state.active_api_key = api_key if api_key else config_module.get_current_api_key(model_name)\n        config_module.update_llm_settings(app_state.active_model_name,\n                                          app_state.active_api_key)  # Update backend config\n        logging.info(\n            f\"Global LLM settings updated: Model={app_state.active_model_name}, Key={'Set' if app_state.active_api_key else 'Not Set/Default'}\")\n        # If you need to update other components (e.g. if there were per-tab selectors) do it here\n        return {shared_api_key_input: app_state.active_api_key}  # Update displayed API key\n\n\n    shared_model_selector.change(update_active_llm_settings, inputs=[shared_model_selector, shared_api_key_input],\n                                 outputs=[shared_api_key_input])\n    shared_api_key_input.change(update_active_llm_settings, inputs=[shared_model_selector, shared_api_key_input],\n                                outputs=[shared_api_key_input])\n\n\n    # CircuitMind (Design) Tab Logic\n    def handle_testbench_upload(file_obj):\n        if file_obj:\n            app_state.testbench_file_path = file_obj.name  # Gradio gives a temp path\n            try:\n                with open(file_obj.name, \"r\", encoding=\"utf-8\") as f:\n                    content = f.read()\n                return content[:2000]  # Preview limit\n            except Exception as e:\n                logging.error(f\"Error reading testbench for preview: {e}\")\n                return f\"Error previewing file: {e}\"\n        return \"No file or error.\"\n\n\n    design_testbench_file.upload(handle_testbench_upload, inputs=[design_testbench_file],\n                                 outputs=[design_testbench_preview])\n\n\n    def handle_expected_output_upload(file_obj):\n        if file_obj:\n            app_state.expected_output_file_path = file_obj.name\n            try:\n                with open(file_obj.name, \"r\", encoding=\"utf-8\") as f:\n                    content = f.read()\n                return content[:2000]  # Preview limit\n            except Exception as e:\n                logging.error(f\"Error reading expected output for preview: {e}\")\n                return f\"Error previewing file: {e}\"\n        return \"No file or error.\"\n\n\n    design_expected_output_file.upload(handle_expected_output_upload, inputs=[design_expected_output_file],\n                                       outputs=[design_expected_output_preview])\n\n\n    def run_workflow_gradio(prompt, tb_file_obj, exp_out_file_obj, current_model, current_api_key,\n                            progress=gr.Progress(track_tqdm=True)):\n        # 0. Initial Setup and Validation\n        if not prompt: yield {workflow_status_display: \"Error: Design prompt is required.\",\n                              design_run_button: gr.update(interactive=True)}; return\n        if not tb_file_obj: yield {workflow_status_display: \"Error: Testbench file is required.\",\n                                   design_run_button: gr.update(interactive=True)}; return\n\n        # Use selected model/key for this run explicitly\n        config_module.update_llm_settings(current_model, current_api_key)\n\n        # Clear previous run's state / displays\n        app_state.agent_interaction_records = []\n        app_state.current_log_last_pos = 0\n        yield {\n            generated_verilog_display: \"\",\n            gate_metrics_display: \"\",\n            workflow_status_display: \"Starting workflow...\",\n            agent_log_display_html: \"<p>Workflow initializing...</p>\",\n            design_run_button: gr.update(interactive=False)  # Disable run button\n        }\n\n        # 1. Setup Experiment Environment (Paths, Directories)\n        experiment_name = f\"gradio_exp_{datetime.now().strftime('%Y%m%d_%H%M%S')}\"\n        experiment_path = os.path.join(config_module.EXPERIMENTS_ROOT, experiment_name)\n        progress.update(0.1)\n\n        # Ensure app_state paths for uploaded files are used if available\n        tb_src_path = tb_file_obj.name if tb_file_obj else None\n        exp_out_src_path = exp_out_file_obj.name if exp_out_file_obj else None\n\n        try:\n            # setup_environment_dummy now also handles log file path in config_module\n            verilog_dir, tb_path_in_exp, _, summary_file_in_exp, exp_out_dir = \\\n                setup_environment_dummy(experiment_path, tb_src_path, exp_out_src_path, prompt)\n            app_state.current_log_file_path = config_module.EXPERIMENT_LOG_FILE  # Get log path from config\n            logging.info(\n                f\"Experiment environment set up in: {experiment_path}. Log file: {app_state.current_log_file_path}\")\n            yield {\n                workflow_status_display: f\"Environment ready. Log: {os.path.basename(app_state.current_log_file_path)}\"}\n        except Exception as e:\n            logging.error(f\"Failed to setup environment: {e}\", exc_info=True)\n            yield {workflow_status_display: f\"Error setting up environment: {e}\",\n                   design_run_button: gr.update(interactive=True)};\n            return\n\n        progress.update(0.2)\n        # 2. Initialize Backend Systems (RAG, Mediator, Agents) - Using Dummies\n        try:\n            rag_system = initialize_rag_system_dummy(\n                config_module.KNOWLEDGE_BASE_PATH, config_module.DETAIL_KNOWLEDGE_BASE_PATH,\n                config_module.EMBEDDING_MODEL, config_module.RAG_LLM_MODEL,  # Main LLM\n                config_module.RAG_INDEX_PATH, config_module.RAG_DATA_PATH,\n                config_module.SUMMARY_JSON_PATH_TEMPLATE.format(model_name=current_model)\n            )\n            mediator = MediatorDummy()\n            # Pass the global config_module to agents\n            user_proxy = AgentDummy(\"UserProxy\", mediator, config_module, rag_system)\n            # Other agents would be initialized here too if used directly in this function:\n            # coder_agent = AgentDummy(\"CoderAgent\", mediator, config_module, rag_system)\n            # reviewer = AgentDummy(\"Reviewer\", mediator, config_module, rag_system)\n            # executor = AgentDummy(\"Executor\", mediator, config_module) # No RAG for executor\n            # summarizer = AgentDummy(\"Summarizer\", mediator, config_module)\n        except Exception as e:\n            logging.error(f\"Failed to initialize backend systems: {e}\", exc_info=True)\n            yield {workflow_status_display: f\"Error initializing backend: {e}\",\n                   design_run_button: gr.update(interactive=True)};\n            return\n\n        # 3. Run the Core Workflow (e.g., user_proxy.submit_design_request)\n        # This part will generate log entries that we need to parse and display\n        main_task_description = \"Running main agent workflow...\"\n        progress.update(0.3)\n        yield {workflow_status_display: main_task_description}\n\n        # Start the process that writes to the log\n        try:\n            # In a real scenario, this might be a non-blocking call or a complex sequence.\n            # For the dummy, it's synchronous but writes to the log via log_agent_activity.\n            user_proxy.submit_design_request(prompt)  # This will trigger dummy agent interactions\n        except Exception as e:\n            logging.error(f\"Error during agent workflow execution: {e}\", exc_info=True)\n            log_agent_activity(\"System\", f\"Workflow error: {e}\")\n            # Continue to try and display logs and partial results if any\n\n        # 4. Periodically Update Agent Log Display and Check for Final Results\n        # The dummy agents log synchronously. In a real async scenario, this loop would poll.\n        # For this refactor, we'll simulate reading the log after the dummy process.\n        # A true streaming approach would involve the backend agents writing to the log,\n        # and this loop `yield`ing updates by reading it.\n\n        # Since dummy logs are written synchronously within submit_design_request,\n        # we process the entire log after it finishes.\n        # For a real long-running process, you'd put `user_proxy.submit_design_request`\n        # in a separate thread/process and poll the log file here in a loop with sleeps.\n\n        # Update agent log display\n        new_records, new_pos, _ = app_state.log_processor.parse_log_to_chat_records(app_state.current_log_file_path,\n                                                                                    app_state.current_log_last_pos)\n        if new_records:\n            app_state.agent_interaction_records.extend(new_records)\n            app_state.current_log_last_pos = new_pos\n        log_html = format_chat_records_to_html(app_state.agent_interaction_records)\n        yield {agent_log_display_html: log_html}\n        progress.update(0.7)\n\n        # 5. Retrieve and Display Final Results\n        progress.update(0.8)\n        final_verilog_code = mediator.get_latest_code()\n        simulation_output_data = mediator.get_latest_simulation_result()\n\n        output_updates = {}\n        if final_verilog_code:\n            output_updates[generated_verilog_display] = final_verilog_code\n            module_name = extract_module_name(final_verilog_code)\n            if module_name:\n                metrics = analyze_verilog_dummy(final_verilog_code, module_name)  # Use dummy\n                metrics_str = f\"Logic Gate Count: {metrics.get('logic_gate_count', 'N/A')}\\n\" \\\n                              f\"Longest Delay: {metrics.get('longest_delay', 'N/A')} ns\\n\" \\\n                              f\"Longest Path: {' -> '.join(metrics.get('longest_path', ['N/A']))}\"\n                if metrics.get(\"error\"): metrics_str = f\"Error: {metrics['error']}\"\n                output_updates[gate_metrics_display] = metrics_str\n            else:\n                output_updates[gate_metrics_display] = \"Could not extract module name for analysis.\"\n        else:\n            output_updates[generated_verilog_display] = \"No Verilog code generated.\"\n            output_updates[gate_metrics_display] = \"No Verilog code to analyze.\"\n\n        sim_status_text = \"Simulation results:\\n\"\n        if simulation_output_data and simulation_output_data.get(\"execution_result\"):\n            actual_sim_out = simulation_output_data.get(\"execution_result\", \"\")\n            sim_message = simulation_output_data.get(\"message\", \"No specific message.\")\n            sim_status_text += f\"Message: {sim_message}\\nActual Output:\\n{actual_sim_out}\\n\"\n\n            # Compare with expected output if available\n            expected_output_content = \"Expected output not loaded.\"\n            if exp_out_src_path:  # If a file was provided\n                try:\n                    with open(exp_out_src_path, 'r', encoding='utf-8') as f:\n                        expected_output_content = f.read().strip()\n                    sim_status_text += f\"\\nExpected Output:\\n{expected_output_content}\\n\"\n                    match, compare_msg = compare_simulation_results(actual_sim_out, expected_output_content)\n                    sim_status_text += f\"Comparison: {compare_msg}\"\n                except Exception as e:\n                    sim_status_text += f\"Could not read/compare expected output: {e}\"\n            else:\n                sim_status_text += \"No expected output file provided for comparison.\"\n        else:\n            sim_status_text += \"No simulation output found.\"\n\n        output_updates[workflow_status_display] = sim_status_text\n        progress.update(1.0)\n        output_updates[design_run_button] = gr.update(interactive=True)  # Re-enable button\n        yield output_updates\n\n\n    design_run_button.click(\n        run_workflow_gradio,\n        inputs=[design_prompt_input, design_testbench_file, design_expected_output_file, shared_model_selector,\n                shared_api_key_input],\n        outputs=[generated_verilog_display, gate_metrics_display, workflow_status_display, agent_log_display_html,\n                 design_run_button]\n    )\n\n\n    # ReasoningV (Chat) Tab Logic\n    def handle_chat_message(message, history, current_model, current_api_key):\n        if not ollama_available:\n            history.append((message, \"Ollama module not found. Cannot connect to LLM.\"))\n            return history, \"\", gr.update(visible=False)\n\n        history.append((message, None))  # Show user message, bot thinking\n        yield {chat_display: history, chat_extracted_verilog_display: \"\",\n               chat_optimize_button: gr.update(visible=False)}\n\n        # Use selected model/key for this chat explicitly\n        # Note: ollama.chat uses 'model' parameter directly, not a global API key for different models usually.\n        # API key handling might be different if not using Ollama or if Ollama is behind a proxy.\n        # For now, assume model name is sufficient for ollama.chat. API key might be for OpenAI models.\n        # The `current_api_key` from shared input is more relevant if calling OpenAI or similar.\n\n        try:\n            # Check if model is OpenAI-like or Ollama-like based on name\n            if \"gpt\" in current_model.lower() or \"openai\" in current_model.lower():\n                # This part would require openai python client and correct API key setup\n                # For now, this is a placeholder for non-ollama models\n                response_content = f\"Model '{current_model}' would need specific API call (e.g. OpenAI). Key: {'Set' if current_api_key else 'Not set'}.\"\n                logging.warning(\"Attempted to use non-Ollama model; specific client needed.\")\n\n            else:  # Assume Ollama model\n                response = ollama.chat(\n                    model=current_model,\n                    messages=[{\"role\": \"user\", \"content\": message}]  # Simpler history for now\n                    # To use full history: messages=[{\"role\": \"user/assistant\", \"content\": h_msg} for h_msg in flat_history]\n                )\n                response_content = response['message']['content']\n\n            history[-1] = (message, response_content)  # Update with bot response\n\n            # Extract Verilog\n            verilog_code_match = re.search(r'```verilog\\n([\\s\\S]*?)\\n```', response_content, re.MULTILINE)\n            extracted_code = \"\"\n            optimize_btn_visible = False\n            if verilog_code_match:\n                extracted_code = verilog_code_match.group(1).strip()\n                optimize_btn_visible = True\n\n            yield {\n                chat_display: history,\n                chat_extracted_verilog_display: extracted_code,\n                chat_optimize_button: gr.update(visible=optimize_btn_visible)\n            }\n\n        except Exception as e:\n            logging.error(f\"Error during chat API call: {e}\", exc_info=True)\n            history[-1] = (message, f\"Error: {str(e)}\")\n            yield {chat_display: history, chat_extracted_verilog_display: \"\",\n                   chat_optimize_button: gr.update(visible=False)}\n\n\n    chat_submit_trigger = chat_message_input.submit(\n        handle_chat_message,\n        inputs=[chat_message_input, chat_display, shared_model_selector, shared_api_key_input],\n        outputs=[chat_display, chat_extracted_verilog_display, chat_optimize_button]\n    )\n    chat_send_button.click(\n        handle_chat_message,\n        inputs=[chat_message_input, chat_display, shared_model_selector, shared_api_key_input],\n        outputs=[chat_display, chat_extracted_verilog_display, chat_optimize_button]\n    )\n    # After message is processed, clear input\n    chat_submit_trigger.then(lambda: gr.update(value=\"\"), outputs=[chat_message_input])\n    # chat_send_button.click event already clears input via the .then() on submit_trigger if wired similarly,\n    # or add another .then() to chat_send_button.click's output if needed.\n    # For simplicity, let's make send_button also clear:\n    chat_send_button.click(lambda: gr.update(value=\"\"), outputs=[chat_message_input])\n\n\n    def action_optimize_in_circuitmind(extracted_verilog_code):\n        # Switch to Design Tab and populate prompt\n        # Gradio doesn't have a direct tab switch function from Python.\n        # The user has to click. We can update the content of the target tab.\n        return {\n            design_prompt_input: extracted_verilog_code,\n            tabs: gr.update(selected=0)  # Request to switch to tab with id 0\n        }\n\n\n    chat_optimize_button.click(\n        action_optimize_in_circuitmind,\n        inputs=[chat_extracted_verilog_display],\n        outputs=[design_prompt_input, tabs]  # Update prompt on Design tab and try to switch\n    )\n\nif __name__ == \"__main__\":\n    # Create dummy icon files if they don't exist for testing avatar paths\n    os.makedirs(\"icons\", exist_ok=True)\n    icon_files_to_check = [\"user.png\", \"coder.png\", \"reviewer.png\", \"executor.png\", \"summarizer.png\", \"default.png\"]\n    for icon_name in icon_files_to_check:\n        dummy_icon_path = os.path.join(\"icons\", icon_name)\n        if not os.path.exists(dummy_icon_path):\n            try:\n                from PIL import Image  # type: ignore\n\n                img = Image.new('RGB', (32, 32), color='red')\n                img.save(dummy_icon_path)\n                logging.info(f\"Created dummy icon: {dummy_icon_path}\")\n            except ImportError:\n                logging.warning(\n                    f\"Pillow not installed. Cannot create dummy icon {dummy_icon_path}. Avatars might not load images.\")\n            except Exception as e_img:\n                logging.error(f\"Error creating dummy icon {dummy_icon_path}: {e_img}\")\n\n    demo.queue().launch(share=False, server_port=7860)"}}}}