Update agent.py

agent.py

@@ -5,29 +5,54 @@ from dotenv import load_dotenv
 # Load environment variables (API keys)
 load_dotenv()
 
-# Initialize the model
-# The evaluation runner might use OpenAI gpt-4o-mini as seen in your logs
-# Ensure OPENAI_API_KEY is set in your environment or .env file
-model = OpenAIModel(model_id="gpt-4o-mini")
+class CustomAgent:
+    def __init__(self):
+        # Hugging Face provides OPENAI_API_KEY as a secret
+        api_key = os.getenv("OPENAI_API_KEY")
+        
+        if api_key:
+            # Using OpenAI gpt-4o-mini as requested/seen in logs
+            self.model = OpenAIModel(model_id="gpt-4o-mini", api_key=api_key)
+        else:
+            # Fallback to Hugging Face Model if no OpenAI key is set
+            self.model = HfApiModel(model_id="Qwen/Qwen2.5-Coder-32B-Instruct")
+            
+        self.search_tool = DuckDuckGoSearchTool()
+        
+        # Initialize the agent
+        # CodeAgent is the best choice for the final project
+        self.agent = CodeAgent(
+            tools=[self.search_tool],
+            model=self.model,
+            add_base_tools=True,
+            max_steps=10
+        )
 
-# You can also use HfApiModel if you prefer Hugging Face models
-# model = HfApiModel(model_id="Qwen/Qwen2.5-Coder-32B-Instruct")
+    def __call__(self, question: str) -> str:
+        """
+        Method called by the evaluation runner (app.py).
+        It MUST return a string.
+        """
+        try:
+            # Defensive check: ensure question is a string
+            if not isinstance(question, str):
+                question = str(question)
+                
+            # Run the agent and capture the result
+            result = self.agent.run(question)
+            
+            # CRITICAL: Always return a string
+            # If the result is a list (which matches your error), this converts it
+            if isinstance(result, list):
+                return " ".join(map(str, result))
+            
+            return str(result)
+            
+        except Exception as e:
+            # Catch and return the error so the whole evaluation loop doesn't break
+            return f"Agent Error: {str(e)}"
 
-# Define tools
-search_tool = DuckDuckGoSearchTool()
-
-# Initialize the agent
-# CodeAgent is generally more powerful for answering questions because it can execute code to verify facts
-agent = CodeAgent(
-    tools=[search_tool],
-    model=model,
-    add_base_tools=True, # Adds tools like 'image_generation', 'transcriber', etc. if needed
-)
-
-# This is the object the evaluation runner will likely import
-# Example: from myagent import agent
+# Standard entry point for testing
 if __name__ == "__main__":
-    # Test your agent locally
-    prompt = "How many studio albums were published by Mercedes Sosa?"
-    response = agent.run(prompt)
-    print(f"Agent Response: {response}")
+    my_agent = CustomAgent()
+    print(my_agent("What is the capital of France?"))