Delete app.py

app.py

@@ -1,221 +0,0 @@
-import os
-import gradio as gr
-import requests
-import pandas as pd
-from dotenv import load_dotenv
-from functions import *
-from langchain_core.messages import HumanMessage
-import traceback
-import time
-
-load_dotenv()
-
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    space_id = os.getenv("SPACE_ID")
-
-    if not profile:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
-    username = profile.username
-    print(f"User logged in: {username}")
-
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
-
-    try:
-        graph = build_graph()
-        agent = graph.invoke
-    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" if space_id else "Repo URL not available"
-    print(f"Agent code repo: {agent_code}")
-
-    # Fetch questions
-    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 Exception as e:
-        print(f"Error fetching questions: {e}")
-        return f"Error fetching questions: {e}", None
-
-    results_log = []
-    answers_payload = []
-
-    print(f"\n{'='*60}")
-    print(f"Running agent on {len(questions_data)} questions...")
-    print(f"{'='*60}\n")
-    
-    # Add delay between questions to avoid rate limiting
-    question_delay = 3.0  # seconds between questions
-    
-    for idx, item in enumerate(questions_data, 1):
-        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
-        
-        # Add delay between questions (except for the first one)
-        if idx > 1:
-            print(f"Waiting {question_delay}s before next question to avoid rate limits...")
-            time.sleep(question_delay)
-        
-        print(f"\n--- Question {idx}/{len(questions_data)} ---")
-        print(f"Task ID: {task_id}")
-        print(f"Question: {question_text}")
-        
-        try:
-            # Add timeout for each question
-            start_time = time.time()
-            input_messages = [HumanMessage(content=question_text)]
-            
-            # Invoke the agent with the question
-            result = agent({"messages": input_messages})
-            
-            # Extract the answer from the result
-            answer = "UNKNOWN"
-            if "messages" in result and result["messages"]:
-                # Look for the last AI message with content
-                for msg in reversed(result["messages"]):
-                    if hasattr(msg, "content") and isinstance(msg.content, str) and msg.content.strip():
-                        # Skip planner outputs
-                        if not any(msg.content.upper().startswith(prefix) for prefix in ["SEARCH:", "CALCULATE:", "DEFINE:", "WIKIPEDIA:", "REVERSE:", "DIRECT:"]):
-                            answer = msg.content.strip()
-                            break
-            
-            elapsed_time = time.time() - start_time
-            print(f"Answer: {answer}")
-            print(f"Time taken: {elapsed_time:.2f}s")
-            
-            answers_payload.append({"task_id": task_id, "submitted_answer": answer})
-            results_log.append({
-                "Task ID": task_id,
-                "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text,
-                "Submitted Answer": answer,
-                "Time (s)": f"{elapsed_time:.2f}"
-            })
-            
-        except Exception as e:
-            print(f"Error running agent on task {task_id}: {e}")
-            print(f"Traceback: {traceback.format_exc()}")
-            
-            # Still submit UNKNOWN for errors
-            answers_payload.append({"task_id": task_id, "submitted_answer": "UNKNOWN"})
-            results_log.append({
-                "Task ID": task_id,
-                "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text,
-                "Submitted Answer": f"ERROR: {str(e)[:50]}",
-                "Time (s)": "N/A"
-            })
-
-    print(f"\n{'='*60}")
-    print(f"Completed processing all questions")
-    print(f"{'='*60}\n")
-
-    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)
-
-    # Summary before submission
-    unknown_count = sum(1 for ans in answers_payload if ans["submitted_answer"] == "UNKNOWN")
-    print(f"\nSummary before submission:")
-    print(f"Total questions: {len(answers_payload)}")
-    print(f"UNKNOWN answers: {unknown_count}")
-    print(f"Attempted answers: {len(answers_payload) - unknown_count}")
-
-    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    print(f"\nSubmitting {len(answers_payload)} answers for user '{username}'...")
-
-    try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
-        result_data = response.json()
-        
-        score = result_data.get('score', 0)
-        correct_count = result_data.get('correct_count', 0)
-        total_attempted = result_data.get('total_attempted', 0)
-        
-        final_status = (
-            f"Submission Successful!\n"
-            f"User: {result_data.get('username')}\n"
-            f"Overall Score: {score}% "
-            f"({correct_count}/{total_attempted} correct)\n"
-            f"Message: {result_data.get('message', 'No message received.')}"
-        )
-        
-        print("\n" + "="*60)
-        print("SUBMISSION RESULTS:")
-        print(f"Score: {score}%")
-        print(f"Correct: {correct_count}/{total_attempted}")
-        print("="*60)
-        
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
-    except Exception as e:
-        status_message = f"Submission Failed: {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-
-# Gradio UI
-with gr.Blocks() as demo:
-    gr.Markdown("# Enhanced GAIA Agent Evaluation Runner")
-    gr.Markdown(
-        """
-        This enhanced agent is optimized for GAIA benchmark questions with improved:
-        - Planning logic for better tool selection
-        - Search capabilities with more comprehensive results
-        - Mathematical expression parsing
-        - Answer extraction from search results
-        - Error handling and logging
-        
-        Target: >50% accuracy on GAIA questions
-        """
-    )
-
-    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__":
-    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("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("SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
-
-    print("-"*(60 + len(" App Starting ")) + "\n")
-
-    print("Launching Gradio Interface for Enhanced GAIA Agent Evaluation...")
-    demo.launch(debug=True, share=False)