Update app.py

app.py

@@ -1,138 +1,182 @@
-import gradio as gr
-import json
 import os
-from datetime import datetime
+import gradio as gr
+import requests
+import inspect
+import pandas as pd
 from agent import GAIAAgent
-from evaluate import evaluate_agent, create_sample_dataset
-import traceback
+from evaluate import extract_final_answer
+
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    """
+    Fetches all questions, runs the GAIAAgent on them, submits all answers,
+    and displays the results.
+    """
+    space_id = os.getenv("SPACE_ID")
 
-def run_evaluation():
-    """Run the GAIA evaluation and return results."""
+    if profile:
+        username = 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"
+
+    # 1. Instantiate Agent
     try:
-        print("Starting GAIA Agent Evaluation...")
-        print("=" * 50)
-        
-        # Initialize agent
         agent = GAIAAgent()
-        
-        # Test API connection first
-        print("Testing xAI API connection...")
-        test_response = agent.test_grok()
-        print(f"API Test Response: {test_response}")
-        
-        # Run evaluation on sample dataset (since we don't have the full GAIA dataset)
-        print("\nRunning evaluation on sample tasks...")
-        score = evaluate_agent(dataset_path=None, max_tasks=10)
-        
-        # Read submission file if it exists
-        submission_content = ""
-        if os.path.exists("submission.jsonl"):
-            with open("submission.jsonl", "r") as f:
-                submission_content = f.read()
-        
-        # Format results
-        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        
-        results = f"""
-# GAIA Agent Evaluation Results
-
-**Timestamp:** {timestamp}
-**Final Score:** {score:.2f}%
-**Certificate Status:** {'✅ ACHIEVED (≥30%)' if score >= 30 else '❌ NOT ACHIEVED (<30%)'}
-
-## API Connection Status
-{test_response}
-
-## Submission File Preview
-```json
-{submission_content[:500]}{'...' if len(submission_content) > 500 else ''}
-```
-
-## Next Steps
-{'🎉 Congratulations! You can now claim your Certificate of Excellence!' if score >= 30 else '💪 Keep improving your agent to reach the 30% threshold.'}
-        """
-        
-        return results, score
-        
     except Exception as e:
-        error_msg = f"""
-# Evaluation Error
+        print(f"Error instantiating agent: {e}")
+        return f"Error initializing agent: {e}", None
 
-**Error:** {str(e)}
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    print(agent_code)
 
-**Traceback:**
-```
-{traceback.format_exc()}
-```
+    # 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
 
-Please check the logs and fix any issues before retrying.
-        """
-        return error_msg, 0.0
-
-def create_interface():
-    """Create the Gradio interface."""
-    
-    with gr.Blocks(title="GAIA Agent Evaluation", theme=gr.themes.Soft()) as demo:
-        gr.Markdown("""
-        # 🤖 GAIA Agent Evaluation
-        
-        This is your GAIA benchmark agent for the Hugging Face Agents Course Certificate of Excellence.
-        
-        **Goal:** Achieve ≥30% score on GAIA benchmark tasks
-        
-        Click the button below to run the evaluation and submit your answers.
-        
-        ⚠️ **Note:** This may take several minutes to complete. Please be patient.
-        """)
-        
-        with gr.Row():
-            run_btn = gr.Button("🚀 Run Evaluation & Submit All Answers", variant="primary", size="lg")
-        
-        with gr.Row():
-            with gr.Column():
-                gr.Markdown("## Run Status / Submission Result")
-                results_output = gr.Markdown("Click the button above to start evaluation...")
-            
-            with gr.Column():
-                gr.Markdown("## Score")
-                score_output = gr.Number(label="Final Score (%)", value=0.0, interactive=False)
-        
-        # Event handler
-        run_btn.click(
-            fn=run_evaluation,
-            inputs=[],
-            outputs=[results_output, score_output],
-            show_progress=True
+    # 3. Run Your Agent
+    results_log = []
+    answers_payload = []
+    print(f"Running agent on {len(questions_data)} questions...")
+    for item in 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
+        try:
+            # Process task with GAIAAgent
+            response = agent.process_task({"task_id": task_id, "question": question_text, "file_name": None})
+            submitted_answer = extract_final_answer(response)
+            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
+        except Exception as e:
+            print(f"Error running agent on task {task_id}: {e}")
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+
+    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)
+
+    # 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.')}"
         )
-        
-        gr.Markdown("""
+        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
+
+# --- Build Gradio Interface using Blocks ---
+with gr.Blocks() as demo:
+    gr.Markdown("# 🤖 GAIA Agent Evaluation")
+    gr.Markdown(
+        """
+        **Instructions:**
+        1. Please clone this space, then modify the code to define your agent's logic, tools, and packages.
+        2. Log in to your Hugging Face account using the button below. This uses your HF username for submission.
+        3. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
+
         ---
-        
-        ## About This Agent
-        
-        - **API:** xAI Grok for reasoning
-        - **Tools:** Web search, file handling, math calculations
-        - **Fallbacks:** Local knowledge for common questions
-        - **Target:** 30% accuracy for certificate eligibility
-        
-        ## Troubleshooting
-        
-        If you encounter issues:
-        1. Check the container logs in the "Logs" tab
-        2. Verify API credentials and internet connectivity
-        3. Ensure all dependencies are installed
-        
-        **Good luck! 🍀**
-        """)
-    
-    return demo
+        **Disclaimers:**
+        Once clicking on the 'submit' button, it can take quite some time (this is the time for the agent to go through all the questions).
+        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution.
+        """
+    )
+
+    gr.LoginButton()
+
+    run_button = gr.Button("🚀 Run Evaluation & Submit All Answers")
+
+    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
+    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+
+    run_button.click(
+        fn=run_and_submit_all,
+        outputs=[status_output, results_table]
+    )
 
 if __name__ == "__main__":
-    # Create and launch the interface
-    demo = create_interface()
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_error=True,
-        show_api=False
-    ) 
+    print("\n" + "-"*30 + " App Starting " + "-"*30)
+    space_host_startup = os.getenv("SPACE_HOST")
+    space_id_startup = os.getenv("SPACE_ID")
+
+    if space_host_startup:
+        print(f"✅ SPACE_HOST found: {space_host_startup}")
+        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
+    else:
+        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
+
+    if space_id_startup:
+        print(f"✅ SPACE_ID found: {space_id_startup}")
+        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
+        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
+    else:
+        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
+
+    print("-"*(60 + len(" App Starting ")) + "\n")
+    print("Launching Gradio Interface for GAIA Agent Evaluation...")
+    demo.launch(debug=True, share=False)