Update app.py

app.py

@@ -72,17 +72,31 @@ class GaiaSmolAgent:
 
     def _generate_plan(self, question: str) -> list[str]:
         """Generates a step-by-step plan to answer the question."""
-        # ... (This method remains unchanged)
         print(f"Generating plan for question: {question[:100]}...")
+        
         prompt = f"""
-        You are an expert planner. Your job is to create a clear, step-by-step plan to answer the given question.
-        The final step must be to output the answer using the `final_answer` tool.
-        Each step should be a clear instruction for an agent to follow.
+        You are a master planner that creates Python code plans for an agent.
+        You have access to the following tools:
+        - `DuckDuckGoSearch(query: str) -> str`: Searches the web and returns a string with the results.
+        - `file_reader(file_path: str) -> str`: Reads a file from a URL or local path and returns its contents as a string.
+        - A full Python interpreter to process strings, perform calculations, etc.
+
+        Your task is to create a plan to answer the user's question. The plan must be a Python list of strings, where each string is a single line of Python code.
+
+        **Crucial Instructions:**
+        1.  The output of `DuckDuckGoSearch` and `file_reader` is always a STRING. You MUST use Python code (e.g., string manipulation, regex) in a subsequent step to extract information from this string. **DO NOT treat the tool output like a dictionary or JSON.**
+        2.  Store the output of tools in variables (e.g., `search_results = DuckDuckGoSearch(...)`).
+        3.  The final step of the plan MUST be a call to `final_answer("your final answer here")`. The answer must be a single, concise string.
 
         Question: "{question}"
 
-        Provide the plan as a Python list of strings. For example:
-        ["Search the web for the latest news on topic X.", "Analyze the search results to find the key person.", "final_answer('The key person is John Doe.')"]
+        Example of a good plan for the question "What is the main topic of the document at http://example.com/paper.pdf?":
+        Plan:
+        [
+            "file_content = file_reader('http://example.com/paper.pdf')",
+            "summary = 'The main topic seems to be about: ' + file_content[:200]",
+            "final_answer(summary)"
+        ]
         """
         response = self.planner_model.generate(prompt)
         print(f"Generated plan: {response}")
@@ -92,10 +106,12 @@ class GaiaSmolAgent:
             if isinstance(plan, list):
                 return plan
             else:
-                return [f"final_answer('Error: Plan generation failed. Could not parse plan: {response}')"]
+                # If the LLM doesn't return a list, create a fallback plan
+                return [f"final_answer('Error: Plan generation failed. The model did not return a valid list.')"]
         except Exception as e:
-            print(f"Error parsing plan: {e}")
-            return [f"final_answer('Error: Plan generation failed. Could not parse plan: {response}')"]
+            print(f"Error parsing plan with eval(): {e}")
+            # If eval fails, create a fallback plan
+            return [f"final_answer('Error: Plan generation failed. The model returned malformed code: {response}')"]
 
 
     def __call__(self, question: str) -> str: