app.py

"""
app.py

This script provides the Gradio web interface to run the evaluation for the
Hugging Face Agents course. It fetches questions from a server, runs the
agent defined in agent.py on them, and submits the answers for scoring.
"""

import os
import re # <-- 1. ADDED IMPORT for Regular Expressions
import gradio as gr
import requests
import pandas as pd

# --- Import your agent's factory function ---
from agent import create_agent_executor # <-- 2. ADDED IMPORT for your agent

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

# --- DELETED BasicAgent class as it's no longer needed ---

# --- 3. ADDED HELPER FUNCTION TO PARSE THE AGENT'S OUTPUT ---
def parse_final_answer(agent_response: str) -> str:
    """
    Extracts the final answer from the agent's full response string.
    The agent is prompted to return 'FINAL ANSWER: [answer]'.
    This function isolates and returns '[answer]'.
    """
    # Use a regular expression to find the text after "FINAL ANSWER:"
    match = re.search(r"FINAL ANSWER:\s*(.*)", agent_response, re.IGNORECASE | re.DOTALL)
    if match:
        # If a match is found, return the captured group, stripped of whitespace
        return match.group(1).strip()
    
    # As a fallback, if the specific format is not found, return the last non-empty line
    lines = [line for line in agent_response.split('\n') if line.strip()]
    if lines:
        return lines[-1].strip()
        
    # If all else fails, return a default message
    return "Could not parse a final answer."


def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the agent on them, submits all answers,
    and displays the results.
    """
    if not profile:
        print("User not logged in.")
        return "Please log in to Hugging Face with the button above to submit.", None
    
    username = 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"
    questions_url = f"{DEFAULT_API_URL}/questions"
    submit_url = f"{DEFAULT_API_URL}/submit"

    # --- 4. MODIFIED AGENT INSTANTIATION AND EXECUTION ---
    
    # 1. Instantiate Agent (using your factory function from agent.py)
    print("Initializing your custom agent...")
    try:
        agent_executor = create_agent_executor(provider="google") # or "groq"
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Fatal Error: Could not initialize agent. Check logs. Details: {e}", None

    # 2. Fetch Questions (this part is correct)
    print(f"Fetching questions from: {questions_url}")
    try:
        response = requests.get(questions_url, timeout=20)
        response.raise_for_status()
        questions_data = response.json()
        print(f"Fetched {len(questions_data)} questions.")
    except Exception as e:
        return f"Error fetching questions: {e}", None

    # 3. Run your Agent (THIS IS THE MOST IMPORTANTLY CORRECTED SECTION)
    results_log = []
    answers_payload = []
    print(f"Running agent 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 data: {item}")
            continue
        
        print(f"\n--- Running Task {i+1}/{len(questions_data)} (ID: {task_id}) ---")
        print(f"Question: {question_text}")
        
        try:
            # CORRECT INVOCATION: Use the agent_executor with the .invoke() method
            # The input must be a dictionary with a "messages" key
            result = agent_executor.invoke({"messages": [("user", question_text)]})
            
            # The agent's final response is in the 'messages' list of the output
            raw_answer = result['messages'][-1].content
            
            # Use our helper function to extract the clean answer
            submitted_answer = parse_final_answer(raw_answer)
            
            print(f"Raw LLM Response: '{raw_answer}'")
            print(f"PARSED FINAL ANSWER: '{submitted_answer}'")

            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"!! AGENT ERROR on task {task_id}: {e}")
             # It's important to log errors so you can see them in the UI
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT RUNTIME ERROR: {e}"})

    if not answers_payload:
        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)

    # 4. Prepare and 5. Submit (these parts are correct)
    submission_data = {"username": username, "agent_code": agent_code, "answers": answers_payload}
    print(f"\nSubmitting {len(answers_payload)} answers for user '{username}'...")
    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)"
        )
        print("Submission successful.")
        return final_status, pd.DataFrame(results_log)
    except Exception as e:
        status_message = f"Submission Failed: {e}"
        print(status_message)
        return status_message, pd.DataFrame(results_log)


# --- Build Gradio Interface using Blocks (This part is correct) ---
with gr.Blocks() as demo:
    gr.Markdown("# Agent Evaluation Runner")
    gr.Markdown("Run your custom agent against the evaluation questions and submit for a score.")
    gr.LoginButton()
    run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary")
    status_output = gr.Textbox(label="Run Status / Submission Result", lines=4, interactive=False)
    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True, max_rows=10)
    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])

if __name__ == "__main__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    # The startup info logs are helpful, so we keep them.
    space_id_startup = os.getenv("SPACE_ID")
    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 environment variable not found (likely running locally).")
    print("-"*(60 + len(" App Starting ")) + "\n")
    print("Launching Gradio Interface...")
    demo.launch()