import os
import gradio as gr
import requests
import pandas as pd
from langchain_core.messages import HumanMessage
from tools import build_graph

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

class BasicAgent:
    
    def __init__(self, csv_file_path: str = "embedding_database.csv"):
        print("BasicAgent initialized.")
        self.graph = build_graph(provider="groq", csv_file_path=csv_file_path)
    
    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        
        # Wrap the question in a HumanMessage
        messages = [HumanMessage(content=question)]
        messages = self.graph.invoke({"messages": messages})
        
        # Extract answer and remove "FINAL ANSWER: " prefix
        answer = messages['messages'][-1].content
        if answer.startswith("FINAL ANSWER: "):
            return answer[14:]
        else:
            # Look for FINAL ANSWER in the response
            lines = answer.split('\n')
            for line in lines:
                if line.strip().startswith("FINAL ANSWER:"):
                    return line.strip()[14:].strip()
            return answer

def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    if not profile:
        print("User not logged in.")
        return "Please Login to Hugging Face with the button.", None
    
    username = f"{profile.username}"
    print(f"User logged in: {username}")
    
    space_id = os.getenv("SPACE_ID")
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "Local Development"
    
    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"
    
    try:
        agent = BasicAgent()  # Make sure your CSV file is named "embeddings.csv" or update the path
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None
    
    # 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:
            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
    
    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
            })
            print(f"Completed task {task_id}: {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:
        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
    
    submission_data = {
        "username": username.strip(), 
        "agent_code": agent_code, 
        "answers": answers_payload
    }
    
    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:
        print(f"Submission failed: {e}")
        status_message = f"Submission Failed: {e}"
        results_df = pd.DataFrame(results_log)
        return status_message, results_df

with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown("""
        **Instructions:**
        1. Make sure your `embeddings.csv` file is in the same directory
        2. Log in to your Hugging Face account using the button below
        3. Click 'Run Evaluation & Submit All Answers' to start the evaluation
        
        **Note:** The evaluation process may take several minutes depending on the number of questions.
    """)
    
    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 + " App Starting " + "-"*30)
    
    space_host = os.getenv("SPACE_HOST")
    space_id = os.getenv("SPACE_ID")
    
    if space_host:
        print(f"✅ SPACE_HOST found: {space_host}")
        print(f"   Runtime URL: https://{space_host}.hf.space")
    else:
        print("ℹ️  Running locally")
    
    if space_id:
        print(f"✅ SPACE_ID found: {space_id}")
        print(f"   Repo URL: https://huggingface.co/spaces/{space_id}")
    
    print("-"*(60 + len(" App Starting ")) + "\n")
    print("Launching Gradio Interface...")
    
    demo.launch(debug=True, share=False)