app.py

#!/usr/bin/env python3
"""
🚀 Enhanced GAIA Agent Interface - Full API Integration
Complete Gradio interface for GAIA benchmark with API connectivity and scoring
"""

import os
import gradio as gr
import json
from datetime import datetime
from gaia_agent import ModularGAIAAgent
import requests
import inspect
import pandas as pd

agent = ModularGAIAAgent()

# (Keep Constants as is)
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

# --- Basic Agent Definition ---
# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
class BasicAgent:
    def __init__(self):
        print("BasicAgent (GAIA Modular Agent) initialized.")
        self.agent = ModularGAIAAgent()
    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        try:
            answer, trace = self.agent.answer_question({"task_id": "manual", "question": question, "file_name": ""})
            print(f"Agent returning answer: {answer}")
            return answer
        except Exception as e:
            print(f"Agent error: {e}")
            return f"AGENT ERROR: {e}"

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 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:
            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, "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.')}"
        )
        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 show_help():
    return (
        "# Agent Capabilities\n"
        "- Multi-modal QA (text, audio, image, code, table, YouTube/video)\n"
        "- File download and analysis from API\n"
        "- Advanced video QA: object detection, captioning, ASR\n"
        "- Secure code execution\n"
        "- Robust error handling and logging\n"
        "- GAIA-compliant output\n"
        "\nSee README.md for full details."
    )

def submit_answers(username, agent_code_url):
    # Placeholder for submission logic
    return f"Submission for {username} with code {agent_code_url} (not implemented in demo)"

def show_leaderboard():
    # Placeholder for leaderboard logic
    return "Leaderboard feature coming soon."

demo = gr.Blocks(title="GAIA Benchmark Agent", theme=gr.themes.Soft())
with demo:
    gr.Markdown("""
    # 🤖 GAIA Benchmark Agent
    Multi-modal, multi-step reasoning agent for the Hugging Face GAIA benchmark.
    """)
    with gr.Tabs():
        with gr.TabItem("API Q&A"):
            api_btn = gr.Button("Run on API Questions", variant="primary")
            api_output = gr.Textbox(label="Answers and Reasoning Trace", lines=20)
            api_btn.click(run_api_questions, outputs=api_output)
        with gr.TabItem("Manual Input"):
            manual_q = gr.Textbox(label="Enter your question", lines=3)
            manual_btn = gr.Button("Answer", variant="primary")
            manual_a = gr.Textbox(label="Answer")
            manual_trace = gr.Textbox(label="Reasoning Trace", lines=5)
            manual_btn.click(run_manual_question, inputs=manual_q, outputs=[manual_a, manual_trace])
        with gr.TabItem("Submission/Leaderboard"):
            username = gr.Textbox(label="Hugging Face Username")
            code_url = gr.Textbox(label="Agent Code URL")
            submit_btn = gr.Button("Submit Answers", variant="primary")
            submit_out = gr.Textbox(label="Submission Result")
            submit_btn.click(submit_answers, inputs=[username, code_url], outputs=submit_out)
            leaderboard_btn = gr.Button("Show Leaderboard")
            leaderboard_out = gr.Textbox(label="Leaderboard")
            leaderboard_btn.click(show_leaderboard, outputs=leaderboard_out)
        with gr.TabItem("Agent Help"):
            help_md = gr.Markdown(show_help())

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 Gradio Interface for Basic Agent Evaluation...")
    demo.launch(debug=True, share=False)