app.py

""" Enhanced Multi-LLM Agent Evaluation Runner with Vector Database Integration"""
import os
import gradio as gr
import requests
import pandas as pd
from langchain_core.messages import HumanMessage
from veryfinal import build_graph, HybridLangGraphMultiLLMSystem

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

# --- Enhanced Agent Definition ---
class EnhancedMultiLLMAgent:
    """A multi-provider LangGraph agent with vector database integration."""
    def __init__(self):
        print("Enhanced Multi-LLM Agent with Vector Database initialized.")
        try:
            self.system = HybridLangGraphMultiLLMSystem(provider="groq")
            self.graph = self.system.graph
            
            # Load metadata if available
            if os.path.exists("metadata.jsonl"):
                print("Loading question metadata...")
                count = self.system.load_metadata_from_jsonl("metadata.jsonl")
                print(f"Loaded {count} questions into vector database")
            
            print("Enhanced Multi-LLM Graph built successfully.")
        except Exception as e:
            print(f"Error building graph: {e}")
            self.graph = None
            self.system = None

    def __call__(self, question: str) -> str:
        print(f"Agent received question: {question[:100]}...")
        
        if self.graph is None or self.system is None:
            return "Error: Agent not properly initialized"
        
        try:
            # Use the enhanced system's process_query method
            answer = self.system.process_query(question)
            
            # Additional validation
            if not answer or answer == question or len(answer.strip()) == 0:
                return "Information not available"
                
            return answer.strip()
                
        except Exception as e:
            error_msg = f"Error: {str(e)}"
            print(error_msg)
            return error_msg

def run_and_submit_all(profile: gr.OAuthProfile | None):
    """Fetch questions, run enhanced agent, and submit answers."""
    space_id = os.getenv("SPACE_ID")
    
    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
    try:
        agent = EnhancedMultiLLMAgent()
        if agent.graph is None:
            return "Error: Failed to initialize agent properly", None
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None
    
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "No space ID available"
    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.")
    except Exception as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None

    # 3. Run Enhanced Agent
    results_log = []
    answers_payload = []
    print(f"Running Enhanced Multi-LLM agent with vector database on {len(questions_data)} questions...")
    
    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}")
        
        try:
            submitted_answer = agent(question_text)
            
            # Additional validation to prevent question repetition
            if submitted_answer == question_text or submitted_answer.startswith(question_text):
                submitted_answer = "Information not available"
            
            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
            results_log.append({
                "Task ID": task_id, 
                "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text, 
                "Submitted Answer": submitted_answer[:200] + "..." if len(submitted_answer) > 200 else submitted_answer
            })
        except Exception as e:
            error_msg = f"AGENT ERROR: {e}"
            print(f"Error running agent on task {task_id}: {e}")
            answers_payload.append({"task_id": task_id, "submitted_answer": error_msg})
            results_log.append({
                "Task ID": task_id, 
                "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text, 
                "Submitted Answer": error_msg
            })

    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"Enhanced Multi-LLM 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 Exception as e:
        status_message = f"Submission Failed: {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df

# --- Build Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# Enhanced Multi-LLM Agent with Vector Database Integration")
    gr.Markdown(
        """
        **Instructions:**
        1. Log in to your Hugging Face account using the button below.
        2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
        
        **Enhanced Agent Features:**
        - **Multi-LLM Support**: Groq (Llama-3 8B/70B, DeepSeek)
        - **Vector Database Integration**: FAISS + Supabase for similar question retrieval
        - **Intelligent Routing**: Automatically selects best provider based on query complexity
        - **Enhanced Tools**: Mathematical operations, web search, Wikipedia integration
        - **Question-Answering**: Optimized for evaluation tasks with proper formatting
        - **Similar Questions Context**: Uses vector similarity to provide relevant context
        - **Error Handling**: Robust fallback mechanisms and comprehensive logging
        
        **Routing Examples:**
        - Math: "What is 25 multiplied by 17?" → Llama-3 70B
        - Search: "Find information about Mercedes Sosa" → Search-Enhanced
        - Complex: "Analyze quantum computing principles" → DeepSeek
        - Simple: "What is the capital of France?" → Llama-3 8B
        
        **Vector Database Features:**
        - Automatic loading of metadata.jsonl if present
        - Similar question retrieval for enhanced context
        - Supabase integration for persistent storage
        - FAISS for fast vector similarity search
        """
    )

    gr.LoginButton()

    run_button = gr.Button("Run 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)

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

if __name__ == "__main__":
    print("\n" + "-"*30 + " Enhanced Multi-LLM Agent with Vector DB Starting " + "-"*30)
    demo.launch(debug=True, share=False)