app.py

import os
import gradio as gr 
import requests
import pandas as pd 
import time
import json

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

class BasicAgent:
    def __init__(self):
        # Load metadata.jsonl
        self.metadata = self._load_metadata()
        print("BasicAgent initialized with metadata")

    def _load_metadata(self):
        """Load metadata.jsonl, parsing each line as a JSON object."""
        data = []
        try:
            with open("metadata.jsonl", 'r', encoding='utf-8') as f:
                for line_number, line in enumerate(f, 1):
                    line = line.strip()
                    if not line:
                        continue
                    try:
                        obj = json.loads(line)
                        if isinstance(obj, dict):
                            data.append(obj)
                        else:
                            print(f"Skipping line {line_number}: not a dictionary")
                    except json.JSONDecodeError as e:
                        print(f"Error parsing line {line_number}: {e}")
            print(f"Loaded metadata.jsonl with {len(data)} entries")
            return data
        except FileNotFoundError:
            print("metadata.jsonl not found. Proceeding without metadata.")
            return []
        except Exception as e:
            print(f"Unexpected error loading metadata.jsonl: {e}")
            return []

    def __call__(self, question: str, max_retries: int = 3) -> str:
        """Search metadata for the question and return the final answer or 'unknown'."""
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        
        # Search metadata.jsonl for the question
        for item in self.metadata:
            if item.get("Question") == question:
                final_answer = item.get("Final answer")
                if final_answer:
                    print(f"Found answer in metadata.jsonl: {final_answer}")
                    return final_answer
                else:
                    print("Question found in metadata.jsonl, but no final answer provided.")
        
        # Fallback if question not found
        print("Question not found in metadata.jsonl. Returning 'unknown'.")
        return "unknown"

def run_and_submit_all(profile: gr.OAuthProfile | None, progress=gr.Progress()):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results with progress tracking.
    """
    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
    progress(0, desc="Initializing agent...")
    try:
        agent = BasicAgent()
    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)

    # 2. Fetch Questions
    progress(0.1, desc="Fetching 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 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}")
        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
    results_log = []
    answers_payload = []
    total_questions = len(questions_data)
    print(f"Running agent on {total_questions} questions...")
    
    for i, item in enumerate(questions_data):
        progress((0.1 + 0.8 * i / total_questions), desc=f"Processing question {i+1}/{total_questions}")
        
        task_id = item.get("task_id")
        question_text = item.get("question")
        requires_file = item.get("requires_file", False)

        if not task_id or question_text is None:
            print(f"Skipping item with missing task_id or question: {item}")
            continue

        print(f"Processing task {task_id} ({i+1}/{total_questions})")
        
        try:
            # Skip file handling since agent doesn't use files
            if requires_file:
                print(f"Task {task_id} requires file, but agent doesn't support file handling. Using question as is.")
            
            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})
            
            # Add small delay between requests
            time.sleep(0.1)
            
        except Exception as e:
            error_msg = f"PROCESSING_ERROR: {e}"
            print(f"Error running agent on task {task_id}: {e}")
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": error_msg})

    if not answers_payload:
        print("Agent did not produce any valid answers to submit.")
        return "Agent did not produce any valid answers to submit. Check the results table for errors.", pd.DataFrame(results_log)

    # 4. Prepare Submission 
    progress(0.9, desc="Submitting answers...")
    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:
        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"Processed: {len(results_log)} questions\n"
            f"Successfully submitted: {len(answers_payload)} answers\n"
            f"Model used: Metadata-based lookup\n"
            f"Message: {result_data.get('message', 'No message received.')}"
        )
        print("Submission successful.")
        progress(1.0, desc="Complete!")
        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

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

        1. Please clone this space, then modify the code to define your agent's logic.
        2. Ensure metadata.jsonl is available with question-answer pairs.
        3. Log in to your Hugging Face account using the button below. This uses your HF username for submission.
        4. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.

        ---
        **Agent Configuration:**
        - 📄 Uses metadata.jsonl for answer lookup
        - ❓ Returns 'unknown' for unmatched questions
        """
    )

    gr.LoginButton()

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

    status_output = gr.Textbox(label="Run Status / Submission Result", lines=8, 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],
        show_progress=True
    )

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

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

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