app.py

import os
import gradio as gr
import requests
import pandas as pd
from crewai import Crew, Process
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

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

# CrewAI components (would normally be in separate files)
class ResearchAgent:
    def __init__(self):
        self.role = "Researcher"
        self.goal = "Research information thoroughly"
    
    def research(self, question):
        # Implement research logic here
        return f"Researched information about: {question}"

class WritingAgent:
    def __init__(self):
        self.role = "Writer"
        self.goal = "Write clear and accurate answers"
    
    def write(self, research_data):
        # Implement writing logic here
        return f"Comprehensive answer based on: {research_data}"

# Enhanced Agent Definition
class CrewAIAgent:
    def __init__(self):
        print("Initializing CrewAI agents...")
        self.researcher = ResearchAgent()
        self.writer = WritingAgent()
        print("CrewAI agents initialized.")

    def __call__(self, question: str) -> str:
        print(f"Processing question: {question[:50]}...")
        
        # Create and execute crew
        try:
            research_data = self.researcher.research(question)
            final_answer = self.writer.write(research_data)
            print(f"Generated answer: {final_answer[:100]}...")
            return final_answer
        except Exception as e:
            print(f"CrewAI error: {e}")
            return f"CrewAI Error: {str(e)}"

def run_and_submit_all(profile: gr.OAuthProfile | None):
    if not profile:
        return "Please log in with Hugging Face first.", None

    # Initialize agent
    try:
        agent = CrewAIAgent()  # Using CrewAI instead of BasicAgent
    except Exception as e:
        return f"Agent initialization failed: {e}", None

    # Fetch questions
    try:
        response = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15)
        questions_data = response.json()
        if not questions_data:
            return "No questions available.", None
    except Exception as e:
        return f"Failed to fetch questions: {e}", None

    # Process questions
    results = []
    answers = []
    for item in questions_data:
        task_id = item.get("task_id")
        question = item.get("question")
        if not task_id or not question:
            continue
            
        try:
            answer = agent(question)
            answers.append({"task_id": task_id, "submitted_answer": answer})
            results.append({
                "Task ID": task_id,
                "Question": question[:100] + "..." if len(question) > 100 else question,
                "Answer": answer[:100] + "..." if len(answer) > 100 else answer
            })
        except Exception as e:
            results.append({
                "Task ID": task_id,
                "Question": question,
                "Answer": f"Error: {str(e)}"
            })

    # Submit answers
    try:
        submission = {
            "username": profile.username,
            "agent_code": f"https://huggingface.co/spaces/{os.getenv('SPACE_ID')}",
            "answers": answers
        }
        response = requests.post(f"{DEFAULT_API_URL}/submit", json=submission, timeout=60)
        result = response.json()
        
        return (
            f"✅ Submitted {len(answers)} answers\n"
            f"📊 Score: {result.get('score', 'N/A')}%\n"
            f"🔢 Correct: {result.get('correct_count', 0)}/{len(answers)}\n"
            f"🤖 Using CrewAI agents",
            pd.DataFrame(results)
        )
    except Exception as e:
        return f"Submission failed: {e}", pd.DataFrame(results)

# Gradio Interface
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🚀 CrewAI Evaluation Runner")
    gr.Markdown("""
    This combines CrewAI agents with the evaluation framework.
    The agents will research and write answers to evaluation questions.
    """)
    
    gr.LoginButton()
    
    with gr.Row():
        run_btn = gr.Button("Run Evaluation", variant="primary")
        
    with gr.Row():
        status = gr.Textbox(label="Status", interactive=False)
        results = gr.DataFrame(label="Results", wrap=True)

    run_btn.click(
        fn=run_and_submit_all,
        outputs=[status, results]
    )

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