app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd

# Import GAIA system from separate module
from gaia_system import BasicAgent, MultiModelGAIASystem

# (Keep Constants as is)
# --- 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 BasicAgent on them, submits all answers,
    and displays the results.
    """
    # --- Determine HF Space Runtime URL and Repo URL ---
    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code

    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"

    # 1. Instantiate Agent ( modify this part to create your agent)
    try:
        agent = BasicAgent()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None
    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
    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 your Agent
    results_log = []
    answers_payload = []
    print(f"Running GAIA-optimized 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:
            # Get raw answer from agent (should be clean already)
            raw_answer = agent(question_text)
            
            # Final cleanup for API submission - ensure no extra formatting
            submitted_answer = clean_for_api_submission(raw_answer)
            
            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})
            print(f"Task {task_id}: {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.')}"
        )
        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

def clean_for_api_submission(answer: str) -> str:
    """
    Final cleanup of agent answers for GAIA API submission
    Ensures exact match compliance
    """
    if not answer:
        return "I cannot determine the answer"
    
    # Remove any remaining formatting artifacts
    answer = answer.strip()
    
    # Remove markdown formatting
    answer = answer.replace('**', '').replace('*', '').replace('`', '')
    
    # Remove any "Answer:" prefixes that might have slipped through
    answer = answer.replace('Answer:', '').replace('ANSWER:', '').strip()
    
    # Remove any trailing periods for factual answers (but keep for sentences)
    if len(answer.split()) == 1 or answer.replace('.', '').replace(',', '').isdigit():
        answer = answer.rstrip('.')
    
    return answer

# --- Enhanced Gradio Interface ---
with gr.Blocks(title="🚀 GAIA Multi-Agent System") as demo:
    gr.Markdown("# 🚀 GAIA Multi-Agent System - BENCHMARK OPTIMIZED")
    gr.Markdown(
        """
        **GAIA Benchmark-Optimized AI Agent for Exact-Match Evaluation**

        This system is specifically optimized for the GAIA benchmark with:
        
        🎯 **Exact-Match Compliance**: Answers formatted for direct evaluation  
        🧮 **Mathematical Precision**: Clean numerical results  
        🌍 **Factual Accuracy**: Direct answers without explanations  
        🔬 **Scientific Knowledge**: Precise values and facts  
        🧠 **Multi-Model Reasoning**: 10+ AI models with intelligent fallback
        
        ---
        **GAIA Benchmark Requirements:**

        ✅ **Direct answers only** - No "The answer is" prefixes  
        ✅ **No reasoning shown** - Thinking process completely removed  
        ✅ **Exact format matching** - Numbers, names, or comma-separated lists  
        ✅ **No explanations** - Just the final result  
        
        **Test Examples:**
        - Math: "What is 15 + 27?" → "42"
        - Geography: "What is the capital of France?" → "Paris"  
        - Science: "How many planets are in our solar system?" → "8"

        ---
        **System Status:**
        - ✅ GAIA-Optimized Agent: Active
        - 🤖 AI Models: DeepSeek-R1, GPT-4o, Llama-3.3-70B + 7 more
        - 🛡️ Fallback System: Enhanced with exact answers
        - 📏 Response Cleaning: Aggressive for benchmark compliance
        """
    )

    # Test interface for local development
    with gr.Row():
        with gr.Column():
            test_input = gr.Textbox(
                label="🧪 Test Question (GAIA Style)",
                placeholder="Try: What is 15 + 27? or What is the capital of France?",
                lines=2
            )
            test_button = gr.Button("🔍 Test Agent", variant="secondary")
        with gr.Column():
            test_output = gr.Textbox(
                label="🤖 Agent Response (Direct Answer Only)",
                lines=3,
                interactive=False
            )

    gr.LoginButton()

    run_button = gr.Button("🚀 Run GAIA Evaluation & Submit All Answers", variant="primary")

    status_output = gr.Textbox(label="📊 Run Status / Submission Result", lines=5, interactive=False)
    results_table = gr.DataFrame(label="📋 Questions and Agent Answers", wrap=True)

    # Test function for local development
    def test_agent(question):
        try:
            agent = BasicAgent()
            response = agent(question)
            # Clean for display (same as API submission)
            cleaned_response = clean_for_api_submission(response)
            return f"Direct Answer: {cleaned_response}"
        except Exception as e:
            return f"Error: {str(e)}"

    test_button.click(
        fn=test_agent,
        inputs=[test_input],
        outputs=[test_output]
    )

    run_button.click(
        fn=run_and_submit_all,
        outputs=[status_output, results_table]
    )

if __name__ == "__main__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    # Check for SPACE_HOST and SPACE_ID at startup for information
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup

    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 repo URLs if SPACE_ID is found
        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 Enhanced GAIA Multi-Agent System...")
    demo.launch(debug=True, share=False)