Update app.py

app.py

@@ -348,6 +348,8 @@ class SlpMultiAgent:
         
     async def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
+        fixed_answer = "This is a default answer."
+        print(f"Agent returning fixed answer: {fixed_answer}")
         
         # Truncate question to avoid exceeding model context length
         MAX_QUESTION_LENGTH = 1000
@@ -371,10 +373,10 @@ class SlpMultiAgent:
         
         # Simplified research agent
         research_agent = CodeAgent(
-            tools=[GoogleSearchTool(), KnowledgeBaseTool(), WikipediaSearchTool(), CodeExecutorTool(), WebScrapeTool()],
+            tools=[GoogleSearchTool(), KnowledgeBaseTool()],
             model=model if not isinstance(model, GeminiModel) else OpenAIServerModel(model_id="gpt-3.5-turbo", temperature=0.0, max_tokens=400),
             additional_authorized_imports=["re", "json"],
-            max_steps=6,
+            max_steps=5,
             name="ResearchAgent",
             verbosity_level=0,
             description="Simple research with Google Search."
@@ -384,23 +386,23 @@ class SlpMultiAgent:
             tools=[MathTool(), GoogleSearchTool()],
             model=model if not isinstance(model, GeminiModel) else OpenAIServerModel(model_id="gpt-3.5-turbo", temperature=0.0, max_tokens=400),
             additional_authorized_imports=["math", "re"],
-            max_steps=6,
+            max_steps=5,
             name="SolverAgent",
             verbosity_level=0,
             description="Simple problem solving with math and search."
         )
 
         manager_agent = CodeAgent(
-            model=model if not isinstance(model, GeminiModel) else OpenAIServerModel(model_id="gpt-3.5-turbo", temperature=0.0, max_tokens=400),
-            tools=[GoogleSearchTool(), MathTool(), FileProcessorTool(), KnowledgeBaseTool(), WikipediaSearchTool(), CodeExecutorTool(), WebScrapeTool()],
+            model=model if not isinstance(model, GeminiModel) else OpenAIServerModel(model_id="gpt-3.5-turbo", temperature=0.0, max_tokens=400),           
+            tools=[GoogleSearchTool(), MathTool(), FileProcessorTool(), KnowledgeBaseTool()],
             managed_agents=[research_agent, solver_agent],
             name="ManagerAgent",
             description="Manager with comprehensive tool access and agent coordination.",
             additional_authorized_imports=["re", "math", "json", "pandas", "numpy", "requests", "time", "os", "tempfile", "uuid"],
             planning_interval=1,
             verbosity_level=0,
-            max_steps=6,
-            final_answer_checks=[check_reasoning]
+            max_steps=8,
+            final_answer_checks=[]
         )
         
         # Create a task for the agent run with retry mechanism for rate limits
@@ -413,57 +415,18 @@ class SlpMultiAgent:
                 result = await loop.run_in_executor(
                     None,
                     lambda: manager_agent.run(f"""
-                    DO NOT output any code blocks, markdown, triple backticks, <code> tags, or print statements. DO NOT output plans, thoughts, or intermediate steps. DO NOT output anything except a single call to final_answer('...').
-                    If you do, you will be penalized and your answer will be rejected.
-                    
-                    INSTRUCTIONS:
-                    - For every question, you MUST call the most appropriate tool to get the answer.
-                    - You MUST NOT output any code blocks, markdown, or print statements.
-                    - You MUST NOT use triple backticks or <code> tags.
-                    - Only call the tool and then call final_answer('...').
-                    - If you cannot answer, call final_answer('I don't know').
-                    - STRICTLY FOLLOW THIS FORMAT. DO NOT OUTPUT ANYTHING ELSE.
-                    - If you reach max steps, call final_answer('I don't know').
-                    
-                    BAD EXAMPLES (DO NOT DO THIS):
-                    ```python\nfinal_answer('foo')\n```
-                    <code>final_answer('foo')</code>
-                    Thoughts: ...
-                    Plan: ...
-                    
-                    GOOD EXAMPLES (DO THIS):
-                    final_answer('Paris')
-                    final_answer('4')
-                    final_answer('I don't know')
+                    Question: {short_question}
                     
-                    Available tools:
-                    - google_search(query): Search the web
-                    - math_calculator(expression): Calculate math
-                    - file_processor(action, data): Handle files
-                    - knowledge_base(topic): Get knowledge
-                    - wikipedia_search(query): Search Wikipedia
-                    - code_executor(code): Execute Python code
-                    - web_scraper(url): Scrape web pages
+                    Answer this question step by step:
                     
-                    EXAMPLES:
-                    Q: What is the capital of France?
-                    A: final_answer('Paris')
-                    Q: 2 + 2 = ?
-                    A: final_answer('4')
-                    Q: Who won the 2020 Olympics 100m?
-                    A: final_answer('Lamont Marcell Jacobs')
-                    Q: What is the square root of 81?
-                    A: final_answer('9')
-                    Q: What is the population of Japan?
-                    A: final_answer('125.7 million')
-                    Q: What is the output of print(2**3)?
-                    A: final_answer('8')
-                    Q: What is the main ingredient in guacamole?
-                    A: final_answer('avocado')
+                    1. If it's a factual question, use google_search() to find current information
+                    2. If it's a math problem, use math_calculator() for calculations
+                    3. Think through the problem logically
+                    4. Provide a clear, specific answer
                     
-                    STRICTLY FOLLOW THIS FORMAT. DO NOT OUTPUT ANYTHING ELSE.
+                    IMPORTANT: Always end with final_answer("your specific answer")
                     
-                    Question: {short_question}
+                    Be precise and factual in your response.
                     """)
                 )
                 break  # Success, exit retry loop
@@ -484,51 +447,33 @@ class SlpMultiAgent:
         if result is None:
             return "I apologize, but I'm currently experiencing technical difficulties. Please try again later."
         
-        # --- Robust post-processing to extract final_answer ---
-        import re
-        def strip_code_blocks(text):
-            # Remove triple backtick code blocks
-            text = re.sub(r"```[\s\S]*?```", "", text)
-            # Remove <code>...</code> blocks
-            text = re.sub(r"<code>[\s\S]*?</code>", "", text)
-            # Remove markdown headers and print statements
-            text = re.sub(r"^#+.*$", "", text, flags=re.MULTILINE)
-            text = re.sub(r"print\(.*?\)", "", text)
-            return text.strip()
-        def extract_final_answer(text):
-            # Try to find final_answer('...') or final_answer("...")
-            match = re.search(r"final_answer\(['\"](.*?)['\"]\)", text, re.DOTALL)
-            if match:
-                return match.group(1).strip()
-            # Try to find final_answer(...) with any content
-            match = re.search(r"final_answer\((.*?)\)", text, re.DOTALL)
-            if match:
-                return match.group(1).strip().strip("'\"")
-            # As a last resort, return the first non-empty line
-            for line in text.splitlines():
-                if line.strip():
-                    return line.strip()
-            return text.strip()
-        # Post-process the result to remove code blocks and extract the answer
+        
+        # Extract clean answer from result
         if result and isinstance(result, str):
-            # If agent reached max steps, return I don't know
-            if "Reached max steps" in result:
-                return "I don't know"
-            cleaned = strip_code_blocks(result)
-            answer = extract_final_answer(cleaned)
-            if answer:
-                # If the answer is still a code block or not a final_answer, fallback
-                if answer.startswith('```') or answer.startswith('<code>') or 'final_answer' not in result:
-                    return "I don't know"
-                return answer
-            # Fallback: try to extract from the original result
-            answer = extract_final_answer(result)
-            if answer:
-                if answer.startswith('```') or answer.startswith('<code>') or 'final_answer' not in result:
-                    return "I don't know"
-                return answer
+            # Look for final_answer pattern - more flexible regex
+            import re
+            final_answer_patterns = [
+                r'final_answer\(["\']([^"\']*)["\'\)]',
+                r'final_answer\("([^"]*)",?\)',
+                r'final_answer\(\'([^\']*)\',?\)',
+                r'final_answer\(([^)]+)\)'
+            ]
+            
+            for pattern in final_answer_patterns:
+                match = re.search(pattern, result, re.IGNORECASE)
+                if match:
+                    clean_answer = match.group(1).strip('"\'')
+                    return clean_answer
+            
+            # If no final_answer found, try to extract the last meaningful line
+            lines = result.strip().split('\n')
+            for line in reversed(lines):
+                line = line.strip()
+                if line and not line.startswith('#') and not line.startswith('###') and len(line) < 200:
+                    return line
+        
         # Return the result from the agent
-        return "I don't know"
+        return result if result else "Unable to determine answer."
     
 def check_reasoning(final_answer, agent_memory):
     # Skip expensive validation to save costs