app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
import aiohttp
import asyncio
import json
from agent import MagAgent
from token_bucket import Limiter, MemoryStorage
import aiofiles
from typing import Optional
import time

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


# Rate limiting configuration
MAX_MODEL_CALLS_PER_MINUTE = 14  # Conservative buffer below 15 RPM
TOKEN_BUCKET_CAPACITY = MAX_MODEL_CALLS_PER_MINUTE
TOKEN_BUCKET_REFILL_RATE = MAX_MODEL_CALLS_PER_MINUTE / 60.0  # Tokens per second


# Initialize global token bucket with MemoryStorage
storage = MemoryStorage()
token_bucket = Limiter(rate=TOKEN_BUCKET_REFILL_RATE, capacity=TOKEN_BUCKET_CAPACITY, storage=storage)

async def check_n_load_attach(session: aiohttp.ClientSession, task_id: str, question: str, api_url: str = "https://agents-course-unit4-scoring.hf.space") -> Optional[str]:
    file_url = f"{api_url}/files/{task_id}"
    try:
        async with session.get(file_url
, timeout=15) as response:
            if response.status == 200:
                content_type = str(response.headers.get("Content-Type", "")).lower()
                content = await response.read()  # Read the file content
                
                # Determine extension based on content_type, content, or question
                extension = await determine_extension(content_type, content, question)
                
                if extension:
                    filename = f"{task_id}{extension}"
                    local_file_path = os.path.join("downloads", filename)
                    os.makedirs("downloads", exist_ok=True)
                    
                    async with aiofiles.open(local_file_path, "wb") as file:
                        await file.write(content)
                    print(f"File downloaded successfully: {local_file_path}")
                    return local_file_path
                else:
                    print(f"Unsupported content type: {content_type} for task {task_id}")
                    return None
            else:
                print(f"Failed to download file for task {task_id}: HTTP {response.status}")
                return None
    except aiohttp.ClientError as e:
        print(f"Error downloading attachment for task {task_id}: {str(e)}")
        return None

async def determine_extension(content_type: str, content: bytes, question
: str) -> Optional[str]:
    # Check if the question mentions Excel
    if "excel" in question.lower():
        # Check for XLS signature
        if content.startswith(b'\xD0\xCF\x11\xE0\xA1\xB1\x1A\xE1'):
            return ".xls"
        # Check for XLSX signature (ZIP archive)
        elif content.startswith(b'\x50\x4B\x03\x04'):
            return ".xlsx"
        else:
            return ".xlsx"  # Default to XLSX if unsure
    # Standard MIME type checks
    if "image/png" in content_type:
        return ".png"
    elif "jpeg" in content_type or "jpg" in content_type:
        return ".jpg"
    elif "spreadsheetml.sheet" in content_type:
        return ".xlsx"
    elif "vnd.ms-excel" in content_type:
        return ".xls"
    elif "audio/mpeg" in content_type:
        return ".mp3"
    elif "application/pdf" in content_type:
        return ".pdf"
    elif "text/x-python" in content_type:
        return ".py"
    else:
        return None


async def fetch_questions(session: aiohttp.ClientSession, questions_url: str) -> list:
    """Fetch questions asynchronously."""
    try:
        async with session.get(questions_url, timeout=15) as response:
            response.raise_for_status()
            questions_data = await response.json()
            if not questions_data:
                print("Fetched questions list is empty.")
                return []
            print(f"Fetched {len(questions_data)} questions.")
            return questions_data
    except aiohttp.ClientError as e:
        print(f"Error fetching questions: {e}")
        return None
    except Exception as e:
        print(f"An unexpected error occurred fetching questions: {e}")
        return None

async def submit_answers(session: aiohttp.ClientSession, submit_url: str, submission_data: dict) -> dict:
    """Submit answers asynchronously."""
    try:
        async with session.post(submit_url, json=submission_data, timeout=60) as response:
            response.raise_for_status()
            return await response.json()
    except aiohttp.ClientResponseError as e:
        print(f"Submission Failed: Server responded with status {e.status}. Detail: {e.message}")
        return None
    except aiohttp.ClientError as e:
        print(f"Submission Failed: Network error - {e}")
        return None

    except Exception as e:
        print(f"An unexpected error occurred during submission: {e}")
        return None

async def process_question(agent, question_text: str, task_id: str, file_path: Optional[str], results_log: list):
    """Process a single question with global rate limiting and retry logic."""
    submitted_answer = None
    max_retries = 4
    retry_delay = 20  # Initial retry delay in seconds
    atimeout = 300

    for attempt in range(max_retries +1):
        try:
            while not token_bucket.consume(1):
                print(f"Rate limit reached for task {task_id}. Waiting to retry...")
                await asyncio.sleep(retry_delay)
            print(f"Processing task {task_id} (attempt {attempt + 1})...")
            submitted_answer = await asyncio.wait_for(
                agent(question_text, file_path),
                timeout=atimeout  # Increased timeout for audio processing
            )
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
            print(f"Completed task {task_id} with answer: {submitted_answer[:50]}...")
            
            ###################                 Addl sleep
            # await asyncio.sleep(retry_delay)
            return {"task_id": task_id, "submitted_answer": submitted_answer}
        except aiohttp.ClientResponseError as e:
            if e.status == 429:
                print(f"Rate limit hit for task {task_id}. Retrying after {retry_delay}s...")
                retry_delay *= 2  # Exponential backoff
                await asyncio.sleep(retry_delay)
                continue
            else:
                submitted_answer = f"AGENT ERROR: {e}"
                results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
                print(f"Failed task {task_id}: {submitted_answer}")
                return None
        except asyncio.TimeoutError:
            submitted_answer = f"AGENT ERROR: Timeout after {atimeout} seconds"
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
            print(f"Failed task {task_id}: {submitted_answer}")
            return None
        except Exception as e:
            submitted_answer = f"AGENT ERROR: {e}"
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
    print(f"Failed task {task_id}: {submitted_answer}")
    return None

async def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions asynchronously, runs the MagAgent on them, submits all answers,
    and displays the results.

    """
    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"

    try:
        agent = MagAgent(rate_limiter=token_bucket)
    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"
    print(agent_code)

    async with aiohttp.ClientSession() as session:
        questions_data = await fetch_questions(session, questions_url)
        if questions_data is None:
            return "Error fetching questions.", None
        if not questions_data:
            return "Fetched questions list is empty or invalid format.", 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

            
            #select question we want to focus
            if "sosa11" in question_text.lower() or "bird" in question_text.lower() or "yankee" in question_text.lower():
                file_path = await check_n_load_attach(session, task_id, question_text.lower())
                result = await process_question(agent, question_text, task_id, file_path, results_log)
                if result:
                    answers_payload.append(result)
            else: 
                print("Skipping unrelated question.")    

        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)

        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)

        result_data = await submit_answers(session, submit_url, submission_data)
        if result_data is None:
            status_message = "Submission Failed."
            print(status_message)
            results_df = pd.DataFrame(results_log)
            return status_message, results_df

        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

# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# Magus Agent Evaluation Runner")
    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, and submit answers.
        ---
        **Notes:**
        The agent uses asynchronous operations for efficiency. Answers are processed and submitted asynchronously.
        """
    )

    gr.LoginButton()

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

    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_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID")

    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(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_HOST environment variable not found (running locally?). Repo URL cannot be determined.")

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

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