app.py

import os
import json
from dotenv import load_dotenv
import gradio as gr
import requests
import inspect
import pandas as pd
from agent import BasicAgent
import time
from datetime import datetime

# Load environment variables from .env file
load_dotenv()

# --- Constants ---
DEFAULT_API_URL = os.getenv('DEFAULT_API_URL', "https://agents-course-unit4-scoring.hf.space")
CHECKPOINT_FILE = "agent_checkpoint.json"

def run_and_submit_all( profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent 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:
        agent = BasicAgent()
    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 ( usefull for others so please keep it public)
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(agent_code)

    # Check for existing checkpoint
    checkpoint_data = None
    if os.path.exists(CHECKPOINT_FILE):
        try:
            with open(CHECKPOINT_FILE, 'r') as f:
                checkpoint_data = json.load(f)
                print(f"Found checkpoint with {len(checkpoint_data.get('questions', []))} questions and {len(checkpoint_data.get('answers', []))} answers")
        except Exception as e:
            print(f"Error loading checkpoint: {e}")
            # If checkpoint is corrupt, remove it
            try:
                os.remove(CHECKPOINT_FILE)
            except:
                pass
            checkpoint_data = None

    # Initialize results tracking
    results_log = []
    answers_payload = []

    if checkpoint_data:
        # If we have a checkpoint, use it
        questions_data = checkpoint_data.get('questions', [])
        # Load any answers we already have
        existing_answers = checkpoint_data.get('answers', [])
        existing_answers_dict = {a.get('task_id'): a.get('submitted_answer') for a in existing_answers}
        print(f"Loaded {len(existing_answers)} existing answers from checkpoint")
        
        # Load existing results log
        if 'results_log' in checkpoint_data:
            results_log = checkpoint_data.get('results_log', [])
        
        # We'll use the checkpoint data
        print(f"Resuming from checkpoint with {len(questions_data)} questions")
    else:
        # 2. Fetch Questions from server
        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.")
            
            # Save questions to checkpoint immediately
            save_checkpoint(questions_data, [], username, [])
            
            # No existing answers
            existing_answers_dict = {}
            
        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 questions we haven't answered yet
    print(f"Running agent on questions...")
    for idx, 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
        
        # Check if the question has an associated file and prepend information
        file_name = item.get("file_name")
        if file_name and file_name != "":
            file_url = f"{api_url}/files/{task_id}"
            question_with_file_info = f"For this task there is file available, with name {file_name}, it's possible to download it from {file_url}\n\n{question_text}"
            question_text = question_with_file_info
        
        # Skip if we already have an answer for this question
        if task_id in existing_answers_dict:
            submitted_answer = existing_answers_dict[task_id]
            print(f"Using cached answer for task_id {task_id}")
            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
            
            # Check if we already have this in results_log
            if not any(r.get("Task ID") == task_id for r in results_log):
                results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
            
            continue
            
        try:
            print(f"Processing question {idx+1}/{len(questions_data)}: {task_id}")
            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})
            
            # Save checkpoint after each answer
            save_checkpoint(questions_data, answers_payload, username, results_log)
            
        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}"})
            
            # Save checkpoint even if there was an error
            save_checkpoint(questions_data, answers_payload, username, results_log)

    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
    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
    try:
        # Check if we're in production mode
        is_production = os.getenv('PRODUCTION_RUN', 'FALSE').upper() == 'TRUE'
        
        if is_production:
            print("Running in PRODUCTION mode - making actual submission")
            response = requests.post(submit_url, json=submission_data, timeout=60)
            response.raise_for_status()
            result_data = response.json()
        else:
            print("Running in SIMULATION mode - generating mock response")
            # Simulate a successful response
            result_data = {
                "username": username,
                "score": 85,
                "correct_count": len(answers_payload) - 2,  # Simulate some incorrect answers
                "total_attempted": len(answers_payload),
                "message": "Simulation mode: This is a mock response"
            }
        
        final_status = (
            f"Submission {'Successful' if is_production else 'Simulated'}!\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(f"Submission {'completed' if is_production else 'simulated'} successfully.")
        
        # Delete checkpoint file after successful submission
        if os.path.exists(CHECKPOINT_FILE):
            try:
                os.remove(CHECKPOINT_FILE)
                print(f"Checkpoint file removed after successful submission")
            except Exception as e:
                print(f"Warning: Could not remove checkpoint file: {e}")
                
        results_df = pd.DataFrame(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

def save_checkpoint(questions_data, answers_payload, username, results_log):
    """Save checkpoint data to a local file."""
    try:
        checkpoint_data = {
            'questions': questions_data,
            'answers': answers_payload,
            'username': username,
            'timestamp': time.time(),
            'results_log': results_log
        }
        
        with open(CHECKPOINT_FILE, 'w') as f:
            json.dump(checkpoint_data, f)
            
        print(f"Checkpoint saved with {len(questions_data)} questions and {len(answers_payload)} answers")
    except Exception as e:
        print(f"Error saving checkpoint: {e}")


# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**

        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.

        ---
        **Disclaimers:**
        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
        """
    )

    gr.LoginButton()

    run_button = gr.Button("Run Evaluation & Submit All Answers")

    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
    # Removed max_rows=10 from DataFrame constructor
    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)
    # Check for SPACE_HOST and SPACE_ID at startup for information
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_raw = os.getenv("SPACE_ID", "")
    
    # Ensure proper SPACE_ID format with username/repo
    if not space_id_raw:
        # Default if completely missing
        space_id_startup = "martinsu/Final_Assignment_Template"
    elif "/" in space_id_raw and not space_id_raw.startswith("/"):
        # Already has proper username/repo format
        space_id_startup = space_id_raw
    elif space_id_raw.startswith("/"):
        # Has a leading slash but missing username
        space_id_startup = f"martinsu{space_id_raw}"
    else:
        # Just repo name without username
        space_id_startup = f"martinsu/{space_id_raw}"

    if space_host_startup:
        print(f"✅ SPACE_HOST found: {space_host_startup}")
        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
    else:
        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")

    if space_id_startup: # Print repo URLs if SPACE_ID is found
        print(f"✅ SPACE_ID found: {space_id_startup}")
        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
    else:
        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")

    # Check for existing checkpoint
    if os.path.exists(CHECKPOINT_FILE):
        try:
            with open(CHECKPOINT_FILE, 'r') as f:
                checkpoint_data = json.load(f)
                print(f"✅ Checkpoint found with {len(checkpoint_data.get('questions', []))} questions and {len(checkpoint_data.get('answers', []))} answers")
                print(f"   Created at: {datetime.fromtimestamp(checkpoint_data.get('timestamp', 0)).strftime('%Y-%m-%d %H:%M:%S')}")
        except Exception as e:
            print(f"⚠️ Checkpoint file exists but could not be read: {e}")
    else:
        print("ℹ️ No checkpoint file found. Will start fresh.")

    print("-"*(60 + len(" App Starting ")) + "\n")

    print("Launching Gradio Interface for Basic Agent Evaluation...")
    demo.launch(debug=True, share=False)