fix

app.py

@@ -19,7 +19,14 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 @tool
 def serper_search(query: str) -> str:
-    """Enhanced search tool optimized for GAIA question types"""
+    """Enhanced search tool optimized for GAIA question types
+    
+    Args:
+        query: The search query to execute
+    
+    Returns:
+        Search results as a formatted string
+    """
     try:
         api_key = os.getenv("SERPER_API_KEY")
         if not api_key:
@@ -75,7 +82,14 @@ def serper_search(query: str) -> str:
 
 @tool
 def math_solver(problem: str) -> str:
-    """Enhanced math solver for GAIA questions"""
+    """Enhanced math solver for GAIA questions
+    
+    Args:
+        problem: The mathematical problem to solve
+    
+    Returns:
+        Solution or analysis of the problem
+    """
     try:
         # Handle chess-related questions
         if "chess" in problem.lower():
@@ -105,7 +119,15 @@ def math_solver(problem: str) -> str:
 
 @tool
 def text_processor(text: str, operation: str = "reverse") -> str:
-    """Enhanced text processing for GAIA questions"""
+    """Enhanced text processing for GAIA questions
+    
+    Args:
+        text: The text to process
+        operation: The operation to perform (reverse, extract, etc.)
+    
+    Returns:
+        Processed text result
+    """
     try:
         # Handle specific reversed text question
         if "ecnetnes siht dnatsrednu uoy fi" in text.lower():
@@ -130,7 +152,15 @@ def text_processor(text: str, operation: str = "reverse") -> str:
 
 @tool
 def data_extractor(source: str, target: str) -> str:
-    """Enhanced data extraction for GAIA questions"""
+    """Enhanced data extraction for GAIA questions
+    
+    Args:
+        source: The source data to extract from
+        target: The type of data to extract
+    
+    Returns:
+        Extracted data as a string
+    """
     try:
         # Handle botanical classification questions
         if "botanical" in target.lower() or "vegetable" in target.lower():
@@ -231,167 +261,135 @@ with gr.Blocks() as demo:
     test_btn.click(test_agent, inputs=question_input, outputs=output)
     
     # Full evaluation handler
-    run_btn.click(run_and_submit_all, outputs=[status, results])
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the GAIA Agent on them, submits all answers,
-    and displays the results.
-    """
-    space_id = os.getenv("SPACE_ID")
+    def run_and_submit_all(profile: gr.OAuthProfile | None):
+        """
+        Fetches all questions, runs the GAIA Agent on them, submits all answers,
+        and displays the results.
+        """
+        space_id = os.getenv("SPACE_ID")
 
-    if profile:
-        username = f"{profile.username}"
-        print(f"User logged in: {username}")
-    else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
+        if profile:
+            username = f"{profile.username}"
+            print(f"User logged in: {username}")
+        else:
+            print("User not logged in.")
+            return "Please Login to Hugging Face with the button.", None
 
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
+        api_url = DEFAULT_API_URL
+        questions_url = f"{api_url}/questions"
+        submit_url = f"{api_url}/submit"
 
-    # 1. Instantiate Agent
-    try:
-        agent = GAIAAgent()
-    except Exception as e:
-        print(f"Error instantiating agent: {e}")
-        return f"Error initializing agent: {e}", None
-
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
-
-    # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
-    try:
-        response = requests.get(questions_url, timeout=15)
-        response.raise_for_status()
-        questions_data = response.json()
-        if not questions_data:
-             print("Fetched questions list is empty.")
-             return "Fetched questions list is empty or invalid format.", None
-        print(f"Fetched {len(questions_data)} questions.")
-    except requests.exceptions.RequestException as e:
-        print(f"Error fetching questions: {e}")
-        return f"Error fetching questions: {e}", None
-    except requests.exceptions.JSONDecodeError as e:
-         print(f"Error decoding JSON response from questions endpoint: {e}")
-         print(f"Response text: {response.text[:500]}")
-         return f"Error decoding server response for questions: {e}", None
-    except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
-
-    # 3. Run Agent
-    results_log = []
-    answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    
-    for i, item in enumerate(questions_data):
-        task_id = item.get("task_id")
-        question_text = item.get("question")
-        if not task_id or question_text is None:
-            print(f"Skipping item with missing task_id or question: {item}")
-            continue
-            
-        print(f"Processing question {i+1}/{len(questions_data)}: {task_id}")
+        # 1. Instantiate Agent
         try:
-            submitted_answer = agent(question_text)
-            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
-            results_log.append({"Task ID": task_id, "Question": question_text[:100] + "...", "Submitted Answer": submitted_answer[:200] + "..."})
-            
-            # Add small delay to avoid rate limiting
-            time.sleep(1)
-            
+            agent = GAIAAgent()
         except Exception as e:
-             print(f"Error running agent on task {task_id}: {e}")
-             results_log.append({"Task ID": task_id, "Question": question_text[:100] + "...", "Submitted Answer": f"AGENT ERROR: {e}"})
+            print(f"Error instantiating agent: {e}")
+            return f"Error initializing agent: {e}", None
 
-    if not answers_payload:
-        print("Agent did not produce any answers to submit.")
-        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
+        agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+        print(agent_code)
 
-    # 4. Prepare Submission 
-    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
-    print(status_update)
-
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
-    try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
-        result_data = response.json()
-        final_status = (
-            f"Submission Successful!\n"
-            f"User: {result_data.get('username')}\n"
-            f"Overall Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
-            f"Message: {result_data.get('message', 'No message received.')}"
-        )
-        print("Submission successful.")
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
-    except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
+        # 2. Fetch Questions
+        print(f"Fetching questions from: {questions_url}")
         try:
-            error_json = e.response.json()
-            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
-        except requests.exceptions.JSONDecodeError:
-            error_detail += f" Response: {e.response.text[:500]}"
-        status_message = f"Submission Failed: {error_detail}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.Timeout:
-        status_message = "Submission Failed: The request timed out."
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.RequestException as e:
-        status_message = f"Submission Failed: Network error - {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except Exception as e:
-        status_message = f"An unexpected error occurred during submission: {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
+            response = requests.get(questions_url, timeout=15)
+            response.raise_for_status()
+            questions_data = response.json()
+            if not questions_data:
+                 print("Fetched questions list is empty.")
+                 return "Fetched questions list is empty or invalid format.", None
+            print(f"Fetched {len(questions_data)} questions.")
+        except requests.exceptions.RequestException as e:
+            print(f"Error fetching questions: {e}")
+            return f"Error fetching questions: {e}", None
+        except requests.exceptions.JSONDecodeError as e:
+             print(f"Error decoding JSON response from questions endpoint: {e}")
+             print(f"Response text: {response.text[:500]}")
+             return f"Error decoding server response for questions: {e}", None
+        except Exception as e:
+            print(f"An unexpected error occurred fetching questions: {e}")
+            return f"An unexpected error occurred fetching questions: {e}", None
 
-# --- Build Gradio Interface ---
-with gr.Blocks() as demo:
-    gr.Markdown("# GAIA Benchmark Agent")
-    gr.Markdown(
-        """
-        **Enhanced Agent for GAIA Benchmark**
-        
-        This agent uses multiple specialized tools to handle diverse question types:
-        - Web search (Serper API + DuckDuckGo)
-        - Wikipedia search
-        - YouTube video analysis
-        - Text processing and reversal
-        - Mathematical problem solving
-        - Data extraction and botanical classification
-        
-        **Instructions:**
-        1. Log in to your Hugging Face account
-        2. Click 'Run Evaluation & Submit All Answers' to start the benchmark
-        3. The agent will process all questions and submit results automatically
+        # 3. Run Agent
+        results_log = []
+        answers_payload = []
+        print(f"Running agent on {len(questions_data)} questions...")
         
-        **Note:** Processing may take several minutes due to the complexity of questions.
-        """
-    )
+        for i, item in enumerate(questions_data):
+            task_id = item.get("task_id")
+            question_text = item.get("question")
+            if not task_id or question_text is None:
+                print(f"Skipping item with missing task_id or question: {item}")
+                continue
+                
+            print(f"Processing question {i+1}/{len(questions_data)}: {task_id}")
+            try:
+                submitted_answer = agent(question_text)
+                answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
+                results_log.append({"Task ID": task_id, "Question": question_text[:100] + "...", "Submitted Answer": submitted_answer[:200] + "..."})
+                
+                # Add small delay to avoid rate limiting
+                time.sleep(1)
+                
+            except Exception as e:
+                 print(f"Error running agent on task {task_id}: {e}")
+                 results_log.append({"Task ID": task_id, "Question": question_text[:100] + "...", "Submitted Answer": f"AGENT ERROR: {e}"})
 
-    gr.LoginButton()
+        if not answers_payload:
+            print("Agent did not produce any answers to submit.")
+            return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-    run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary")
+        # 4. Prepare Submission 
+        submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
+        status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
+        print(status_update)
 
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+        # 5. Submit
+        print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+        try:
+            response = requests.post(submit_url, json=submission_data, timeout=60)
+            response.raise_for_status()
+            result_data = response.json()
+            final_status = (
+                f"Submission Successful!\n"
+                f"User: {result_data.get('username')}\n"
+                f"Overall Score: {result_data.get('score', 'N/A')}% "
+                f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
+                f"Message: {result_data.get('message', 'No message received.')}"
+            )
+            print("Submission successful.")
+            results_df = pd.DataFrame(results_log)
+            return final_status, results_df
+        except requests.exceptions.HTTPError as e:
+            error_detail = f"Server responded with status {e.response.status_code}."
+            try:
+                error_json = e.response.json()
+                error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
+            except requests.exceptions.JSONDecodeError:
+                error_detail += f" Response: {e.response.text[:500]}"
+            status_message = f"Submission Failed: {error_detail}"
+            print(status_message)
+            results_df = pd.DataFrame(results_log)
+            return status_message, results_df
+        except requests.exceptions.Timeout:
+            status_message = "Submission Failed: The request timed out."
+            print(status_message)
+            results_df = pd.DataFrame(results_log)
+            return status_message, results_df
+        except requests.exceptions.RequestException as e:
+            status_message = f"Submission Failed: Network error - {e}"
+            print(status_message)
+            results_df = pd.DataFrame(results_log)
+            return status_message, results_df
+        except Exception as e:
+            status_message = f"An unexpected error occurred during submission: {e}"
+            print(status_message)
+            results_df = pd.DataFrame(results_log)
+            return status_message, results_df
 
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
+    run_btn.click(
+        run_and_submit_all,
+        outputs=[status, results]
     )
 
 if __name__ == "__main__":