app.py

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

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

# --- Enhanced Agent Definition ---
# ----- THIS IS WHERE YOU CAN BUILD WHAT YOU WANT ------
class GIAIAAgent:
    """
    Agent designed to answer GIAIA questions.
    Modify this class to implement your own logic for answering questions.
    """
    
    def __init__(self):
        """Initialize your agent with any necessary tools, models, or resources."""
        print("GIAIA Agent initialized.")
        
        # TODO: Initialize your tools, models, or APIs here
        # Example:
        # self.model = load_your_model()
        # self.tools = load_your_tools()
        
        # You can store a cache of answers if needed
        self.answer_cache = {}
        
    def __call__(self, question: str) -> str:
        """
        Process a question and return an answer.
        
        Args:
            question: The question text to answer
            
        Returns:
            The answer as a string
        """
        print(f"Processing question (first 100 chars): {question[:100]}...")
        
        # TODO: Implement your actual question-answering logic here
        # This is where you should put your agent's intelligence
        
        # For now, let's do some basic processing to show the structure
        try:
            # You might want to:
            # 1. Parse the question
            # 2. Use tools to gather information
            # 3. Process with a model
            # 4. Format the answer
            
            # Example structure (replace with your actual logic):
            answer = self._generate_answer(question)
            
            print(f"Generated answer: {answer[:50]}...")
            return answer
            
        except Exception as e:
            print(f"Error processing question: {e}")
            return f"Error generating answer: {str(e)}"
    
    def _generate_answer(self, question: str) -> str:
        """
        Internal method to generate answers.
        Replace this with your actual implementation.
        
        This is a placeholder - you should implement your own logic!
        """
        
        # TODO: IMPLEMENT YOUR ACTUAL ANSWER GENERATION LOGIC HERE
        # 
        # Some ideas:
        # - Use a language model via API
        # - Use retrieval augmented generation
        # - Use web search tools
        # - Use a knowledge base
        # - Implement specific logic for each type of question
        
        # For demonstration, I'll categorize questions based on keywords
        # BUT YOU SHOULD REPLACE THIS WITH YOUR ACTUAL IMPLEMENTATION
        
        question_lower = question.lower()
        
        # This is just a simple example - REPLACE WITH REAL LOGIC!
        if "what is" in question_lower:
            return f"Based on the context, {question.replace('What is', '').strip()} refers to a concept in the field."
        elif "how to" in question_lower:
            return f"To {question.replace('How to', '').strip()}, you should follow these steps: [Your solution here]"
        elif "explain" in question_lower:
            return f"Here's an explanation of {question.replace('Explain', '').strip()}: [Your explanation here]"
        elif "difference between" in question_lower:
            return f"The main differences are: [Your comparison here]"
        else:
            # For questions without clear keywords, you might want to use a default approach
            return f"Answer: [Your answer for: {question[:50]}...]"
    
    def batch_answer(self, questions: List[str]) -> List[str]:
        """
        Optional: Process multiple questions at once for efficiency.
        
        Args:
            questions: List of question strings
            
        Returns:
            List of answer strings
        """
        answers = []
        for question in questions:
            answers.append(self(question))
        return answers


def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the GIAIAAgent 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:
        # Use the enhanced GIAIA agent instead of BasicAgent
        agent = GIAIAAgent()
        print("Agent instantiated successfully")
    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
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "Local development"
    print(f"Agent code URL: {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.")
        
        # Optional: Display the first few questions to see what we're dealing with
        print("\n--- First 3 questions (preview) ---")
        for i, item in enumerate(questions_data[:3]):
            print(f"Q{i+1}: {item.get('question', 'No question')[:100]}...")
        print("--- End preview ---\n")
        
    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 on all questions
    results_log = []
    answers_payload = []
    
    print(f"\nRunning GIAIA agent on {len(questions_data)} questions...")
    print("This may take a while depending on your implementation...")
    
    # Process questions one by one (or in batches if you implement batch_answer)
    for i, item in enumerate(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
        
        print(f"Processing question {i+1}/{len(questions_data)} (Task ID: {task_id})")
        
        try:
            # Run your agent on the question
            submitted_answer = agent(question_text)
            
            # Add to payload for submission
            answers_payload.append({
                "task_id": task_id, 
                "submitted_answer": submitted_answer
            })
            
            # Log for display
            results_log.append({
                "Task ID": task_id, 
                "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text,
                "Submitted Answer": submitted_answer[:100] + "..." if len(submitted_answer) > 100 else submitted_answer
            })
            
            print(f"✓ Question {i+1} answered")
            
        except Exception as e:
            print(f"✗ Error running agent on task {task_id}: {e}")
            results_log.append({
                "Task ID": task_id, 
                "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text,
                "Submitted Answer": f"AGENT ERROR: {str(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 answers to scoring server
    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.")
        print(f"Score: {result_data.get('score', 'N/A')}%")
        
        # Create full results DataFrame with complete answers for download
        full_results_log = []
        for i, item in enumerate(questions_data):
            if i < len(answers_payload):
                full_results_log.append({
                    "Task ID": item.get("task_id"),
                    "Question": item.get("question"),
                    "Submitted Answer": answers_payload[i].get("submitted_answer")
                })
        
        results_df = pd.DataFrame(full_results_log if full_results_log else 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(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# GIAIA Agent Evaluation Runner")
    gr.Markdown(
        """
        **Welcome to the GIAIA Agent Evaluation!**
        
        This space evaluates your agent on 20 GIAIA questions.
        
        **Instructions:**
        1.  **Fork/Clone** this space to your own account
        2.  **Modify the `GIAIAAgent` class** in `app.py` to implement your agent's logic
        3.  Add any required **dependencies** to `requirements.txt`
        4.  Log in with your Hugging Face account below
        5.  Click 'Run Evaluation' to test your agent on all 20 questions
        6.  View your score and detailed results
        
        **Tips for Implementation:**
        - The agent will be called once for each question
        - You can add tools, use APIs, or implement any logic you want
        - Consider performance - all 20 questions will be processed sequentially
        - You can implement caching if needed
        
        **Disclaimers:**
        - This evaluation may take some time depending on your implementation
        - Make sure to keep your space public so others can see your solution
        """
    )

    with gr.Row():
        with gr.Column(scale=1):
            gr.LoginButton()
            
        with gr.Column(scale=2):
            run_button = gr.Button("🚀 Run Evaluation on 20 Questions", variant="primary", size="lg")

    with gr.Row():
        with gr.Column():
            status_output = gr.Textbox(
                label="Run Status / Submission Result", 
                lines=6, 
                interactive=False,
                placeholder="Status will appear here..."
            )
    
    with gr.Row():
        with gr.Column():
            results_table = gr.DataFrame(
                label="Questions and Agent Answers (Preview)", 
                wrap=True,
                height=400
            )
    
    with gr.Row():
        with gr.Column():
            gr.Markdown(
                """
                ---
                **Need Help?**
                - Check the [documentation](https://huggingface.co/docs)
                - Modify the `GIAIAAgent._generate_answer` method with your logic
                - Add any required packages to `requirements.txt`
                """
            )

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

if __name__ == "__main__":
    print("\n" + "="*70)
    print(" GIAIA Agent Evaluation App Starting")
    print("="*70)
    
    # Check for SPACE_HOST and SPACE_ID at startup
    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: https://{space_host_startup}.hf.space")
    else:
        print("ℹ️  SPACE_HOST 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}")
    else:
        print("ℹ️  SPACE_ID not found (running locally)")

    print("="*70 + "\n")
    print("Launching Gradio Interface...")
    print("NOTE: The agent in this template uses placeholder logic.")
    print("You MUST modify the GIAIAAgent class to implement actual answers!")
    print("-"*70 + "\n")
    
    demo.launch(debug=True, share=False)