Update app.py

app.py

@@ -333,91 +333,91 @@ def should_continue(state: AgentState):
 
 # --- Basic Agent Definition ---
 class BasicAgent:
-def __init__(self):
-        print("BasicAgent (LangGraph) initializing...")
-        GROQ_API_KEY = os.getenv("GROQ_API_KEY")
-        if not GROQ_API_KEY: raise ValueError("GROQ_API_KEY secret is not set!")
-
-        self.tools = defined_tools
-        
-        # Build tool descriptions separately to avoid f-string backslash issues
-        tool_desc_list = []
-        for tool in self.tools:
-            if tool.name == 'code_interpreter':
-                desc = (f"- {tool.name}: Executes Python code. Use for calculations, data manipulation, or logic puzzles.\n"
-                        f"  **CODE INTERPRETER RULES:**\n"
-                        f"  1. ALWAYS use `print()` for final results.\n"
-                        f"  2. Write SIMPLE, single-step scripts.\n"
-                        f"  3. PLAN your next script using plain text output first.\n"
-                        f"  4. Write reasoning as Python comments (#) before code.\n"
-                        f"  'pandas' (as pd) is available.")
-            else:
-                desc = f"- {tool.name}: {tool.description}"
-            tool_desc_list.append(desc)
-        
-        tool_descriptions = "\n".join(tool_desc_list)
-
-        # ==================== SYSTEM PROMPT V4 ====================
-        self.system_prompt = f"""You are a highly intelligent and meticulous AI assistant for the GAIA benchmark.
-Your goal is to provide the concise, factual answer by strictly following a step-by-step reasoning process.
-
-**CRITICAL PROTOCOL: YOU MUST FOLLOW THIS PROCESS**
-
-1.  **ANALYZE:** Read the question and all messages in the history.
-2.  **MANDATORY FIRST STEP:** Your *first* response on *any* new task MUST be a plan in plain text. Do NOT call any tool on your first turn. Write down your logic, what you need, and which tool you *plan* to use next. Failure to provide a plan first will result in incorrect behavior.
-3.  **EXECUTE:** After submitting your plan, you will run again. Now, execute the *next* step of your plan by calling the *one* appropriate tool using the correct JSON format.
-4.  **ANALYZE TOOL OUTPUT:** You will receive a ToolMessage with the output. You MUST read it carefully.
-5.  **REPEAT or FINISH:**
-    * **If more steps are needed:** Go back to step 1 (ANALYZE the new info & PLAN). Write an *updated* plan as plain text (e.g., "The search found X. My next step is to use code_interpreter to process X...").
-    * **If the ToolMessage contains the final answer:** You MUST call the `final_answer_tool`. Your answer *must* be derived *only* from the ToolMessage output, not your own knowledge.
-
-**RULES:**
-* **NEVER** call a tool on the same turn you write a plan (plain text).
-* **NEVER** use your pre-trained "leaked" knowledge for the final answer. The answer *must* come from a ToolMessage (e.g., from `code_interpreter`'s print() or `search_tool`).
-* **NEVER** answer a logic puzzle from memory. You *must* use `code_interpreter`, ensure it `print()`s the result, analyze that output, and then use that printed result for `final_answer_tool`.
-* **NEVER** call `final_answer_tool` until a tool has explicitly given you the answer in its output.
-* **Error Handling:** If a tool call returns an Error, your next step (Step 1 PLAN) MUST analyze the error message and propose a *different* approach (different tool, different arguments, different logic). Do not retry the exact same failed call.
-
-**TOOLS:**
-{tool_descriptions}
-
-**TOOL FORMAT (JSON ONLY):**
-Respond ONLY with a single JSON block like this when calling a tool:
-```json
-{{
-  "tool": "tool_name",
-  "tool_input": {{ "arg_name1": "value1", ... }}
-}}
-```
-* Replace `tool_name` with the tool's name. Provide arguments in `tool_input`. Match names/types precisely.
-* Do not add any text before or after the JSON block.
-
-Example for final_answer_tool:
-```json
-{{
-  "tool": "final_answer_tool",
-  "tool_input": {{
-    "answer": "The final answer string here"
-  }}
-}}
-```
-NOTE: The value for "answer" MUST be a string enclosed in double quotes.
-"""
-
-        print("Initializing Groq LLM Endpoint...")
-        try:
-            chat_llm = ChatGroq(
-                temperature=0.01, # Low temperature for factual tasks
-                groq_api_key=GROQ_API_KEY,
-                model_name="openai/gpt-oss-120b" # <-- Switched Model
-            )
-            print("✅ Groq LLM Endpoint initialized with openai/gpt-oss-120b.")
-        except Exception as e: 
-            print(f"Error initializing Groq: {e}")
-            raise
-        
-        self.llm_with_tools = chat_llm.bind_tools(self.tools)
-        print("✅ Tools bound to LLM (using bind_tools).")
+    def __init__(self):
+            print("BasicAgent (LangGraph) initializing...")
+            GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+            if not GROQ_API_KEY: raise ValueError("GROQ_API_KEY secret is not set!")
+    
+            self.tools = defined_tools
+            
+            # Build tool descriptions separately to avoid f-string backslash issues
+            tool_desc_list = []
+            for tool in self.tools:
+                if tool.name == 'code_interpreter':
+                    desc = (f"- {tool.name}: Executes Python code. Use for calculations, data manipulation, or logic puzzles.\n"
+                            f"  **CODE INTERPRETER RULES:**\n"
+                            f"  1. ALWAYS use `print()` for final results.\n"
+                            f"  2. Write SIMPLE, single-step scripts.\n"
+                            f"  3. PLAN your next script using plain text output first.\n"
+                            f"  4. Write reasoning as Python comments (#) before code.\n"
+                            f"  'pandas' (as pd) is available.")
+                else:
+                    desc = f"- {tool.name}: {tool.description}"
+                tool_desc_list.append(desc)
+            
+            tool_descriptions = "\n".join(tool_desc_list)
+    
+            # ==================== SYSTEM PROMPT V4 ====================
+            self.system_prompt = f"""You are a highly intelligent and meticulous AI assistant for the GAIA benchmark.
+    Your goal is to provide the concise, factual answer by strictly following a step-by-step reasoning process.
+    
+    **CRITICAL PROTOCOL: YOU MUST FOLLOW THIS PROCESS**
+    
+    1.  **ANALYZE:** Read the question and all messages in the history.
+    2.  **MANDATORY FIRST STEP:** Your *first* response on *any* new task MUST be a plan in plain text. Do NOT call any tool on your first turn. Write down your logic, what you need, and which tool you *plan* to use next. Failure to provide a plan first will result in incorrect behavior.
+    3.  **EXECUTE:** After submitting your plan, you will run again. Now, execute the *next* step of your plan by calling the *one* appropriate tool using the correct JSON format.
+    4.  **ANALYZE TOOL OUTPUT:** You will receive a ToolMessage with the output. You MUST read it carefully.
+    5.  **REPEAT or FINISH:**
+        * **If more steps are needed:** Go back to step 1 (ANALYZE the new info & PLAN). Write an *updated* plan as plain text (e.g., "The search found X. My next step is to use code_interpreter to process X...").
+        * **If the ToolMessage contains the final answer:** You MUST call the `final_answer_tool`. Your answer *must* be derived *only* from the ToolMessage output, not your own knowledge.
+    
+    **RULES:**
+    * **NEVER** call a tool on the same turn you write a plan (plain text).
+    * **NEVER** use your pre-trained "leaked" knowledge for the final answer. The answer *must* come from a ToolMessage (e.g., from `code_interpreter`'s print() or `search_tool`).
+    * **NEVER** answer a logic puzzle from memory. You *must* use `code_interpreter`, ensure it `print()`s the result, analyze that output, and then use that printed result for `final_answer_tool`.
+    * **NEVER** call `final_answer_tool` until a tool has explicitly given you the answer in its output.
+    * **Error Handling:** If a tool call returns an Error, your next step (Step 1 PLAN) MUST analyze the error message and propose a *different* approach (different tool, different arguments, different logic). Do not retry the exact same failed call.
+    
+    **TOOLS:**
+    {tool_descriptions}
+    
+    **TOOL FORMAT (JSON ONLY):**
+    Respond ONLY with a single JSON block like this when calling a tool:
+    ```json
+    {{
+      "tool": "tool_name",
+      "tool_input": {{ "arg_name1": "value1", ... }}
+    }}
+    ```
+    * Replace `tool_name` with the tool's name. Provide arguments in `tool_input`. Match names/types precisely.
+    * Do not add any text before or after the JSON block.
+    
+    Example for final_answer_tool:
+    ```json
+    {{
+      "tool": "final_answer_tool",
+      "tool_input": {{
+        "answer": "The final answer string here"
+      }}
+    }}
+    ```
+    NOTE: The value for "answer" MUST be a string enclosed in double quotes.
+    """
+    
+            print("Initializing Groq LLM Endpoint...")
+            try:
+                chat_llm = ChatGroq(
+                    temperature=0.01, # Low temperature for factual tasks
+                    groq_api_key=GROQ_API_KEY,
+                    model_name="openai/gpt-oss-120b" # <-- Switched Model
+                )
+                print("✅ Groq LLM Endpoint initialized with openai/gpt-oss-120b.")
+            except Exception as e: 
+                print(f"Error initializing Groq: {e}")
+                raise
+            
+            self.llm_with_tools = chat_llm.bind_tools(self.tools)
+            print("✅ Tools bound to LLM (using bind_tools).")
  
     # --- Agent Node with Robust Parsing Fallback ---
     def agent_node(state: AgentState):