app.py

import os
import gradio as gr
import requests
import pandas as pd
import json
import re
from typing import List, Dict, Any, Optional

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

# --- Simple GAIA Agent Definition ---
class SimpleGAIAAgent:
    def __init__(self):
        print("SimpleGAIAAgent initialized.")
        
    def __call__(self, question: str) -> str:
        """Main method to process questions and generate answers"""
        print(f"Agent received question: {question}")
        
        # Basic question analysis
        question_lower = question.lower()
        
        # Handle calculation questions
        if any(keyword in question_lower for keyword in ["calculate", "compute", "sum", "difference", "product", "divide"]):
            # Extract numbers
            numbers = re.findall(r'\d+', question)
            if len(numbers) >= 2:
                if "sum" in question_lower or "add" in question_lower or "plus" in question_lower:
                    result = sum(int(num) for num in numbers)
                    return f"The sum of the numbers is {result}"
                elif "difference" in question_lower or "subtract" in question_lower or "minus" in question_lower:
                    result = int(numbers[0]) - int(numbers[1])
                    return f"The difference between {numbers[0]} and {numbers[1]} is {result}"
                elif "product" in question_lower or "multiply" in question_lower:
                    result = int(numbers[0]) * int(numbers[1])
                    return f"The product of {numbers[0]} and {numbers[1]} is {result}"
                elif "divide" in question_lower:
                    if int(numbers[1]) != 0:
                        result = int(numbers[0]) / int(numbers[1])
                        return f"The result of dividing {numbers[0]} by {numbers[1]} is {result}"
                    else:
                        return "Cannot divide by zero"
            return "I'll calculate this for you: " + question
        
        # Handle image analysis questions
        elif any(keyword in question_lower for keyword in ["image", "picture", "photo", "graph", "chart"]):
            return "Based on the image, I can see several key elements that help answer your question. The main subject appears to be [description] which indicates [answer]."
        
        # Handle factual questions
        elif any(keyword in question_lower for keyword in ["who", "what", "where", "when", "why", "how"]):
            if "who" in question_lower:
                return "The person involved is a notable figure in this field with significant contributions and achievements."
            elif "when" in question_lower:
                return "This occurred during a significant historical period, specifically in the early part of the relevant era."
            elif "where" in question_lower:
                return "The location is in a region known for its historical and cultural significance."
            elif "what" in question_lower:
                return "This refers to an important concept or entity that has several key characteristics and functions."
            elif "why" in question_lower:
                return "This happened due to a combination of factors including historical context, individual decisions, and broader societal trends."
            elif "how" in question_lower:
                return "The process involves several key steps that must be followed in sequence to achieve the desired outcome."
        
        # General knowledge questions
        else:
            return "Based on my analysis, the answer to your question involves several important factors. First, we need to consider the context and specific details mentioned. Taking all available information into account, the most accurate response would be a comprehensive explanation that addresses all aspects of your query."

# FIXED FUNCTION: Added *args to handle extra arguments from Gradio
def run_and_submit_all(profile: gr.OAuthProfile | None, *args):
    """
    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 = SimpleGAIAAgent()
    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('overall_score', 'N/A')}\n"
            f"Correct Answers: {result_data.get('correct_answers', 'N/A')}\n"
            f"Total Questions: {result_data.get('total_questions', 'N/A')}\n"
        )
        print(final_status)
        return final_status, pd.DataFrame(results_log)
    except requests.exceptions.RequestException as e:
        error_msg = f"Error submitting answers: {e}"
        print(error_msg)
        return error_msg, pd.DataFrame(results_log)
    except Exception as e:
        error_msg = f"An unexpected error occurred during submission: {e}"
        print(error_msg)
        return error_msg, pd.DataFrame(results_log)

# --- Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    
    gr.Markdown("Instructions:")
    gr.Markdown("1. Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...")
    gr.Markdown("2. Log in to your Hugging Face account using the button below. This uses your HF username for submission.")
    gr.Markdown("3. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.")
    
    gr.Markdown("---")
    
    gr.Markdown("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. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.")
    
    with gr.Row():
        login_button = gr.LoginButton(value="Sign in with Hugging Face")
    
    with gr.Row():
        submit_button = gr.Button("Run Evaluation & Submit All Answers")
    
    with gr.Row():
        with gr.Column():
            output_status = gr.Textbox(label="Run Status / Submission Result")
            output_results = gr.Dataframe(label="Questions and Agent Answers")
    
    submit_button.click(run_and_submit_all, inputs=[login_button], outputs=[output_status, output_results])

if __name__ == "__main__":
    demo.launch()