app.py

import os
import gradio as gr
import requests
import pandas as pd
from my_agent import GeminiAgentContainer
from markdownify import markdownify as to_markdown
import time
import json

# (Keep Constants as is)
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"


# --- Global Variables ---
questions = None
results_log = []
answers_by_task = {}

def load_questions(questions_url):
    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 or invalid.")
             return None
        print(f"Fetched {len(questions_data)} questions.")
    except requests.exceptions.RequestException as e:
        print(f"Error fetching questions: {e}")
        return 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 None
    except Exception as e:
        print(f"An unexpected error occurred fetching questions: {e}")
        return None
    return questions_data

def answer_one(agent, question_data):
    """
    Runs the agent on a single question and returns the result.
    """
    task_id = question_data.get("task_id")
    question_text = question_data.get("question")
    filename = question_data.get("file_name")
    payload = None
    submitted_answer = None
    agent_error = None

    try:
        if not task_id or question_text is None:
            raise ValueError(f"Missing task_id or question in item: {question_data}")
        if filename:
            file_prompt = f"\nThere is an attached file with task id `{task_id}` available.\n"
            question_text = file_prompt + question_text
        submitted_answer = agent(question_text)
        payload = {"task_id": task_id, "submitted_answer": submitted_answer}
    except Exception as e:
         print(agent)
         print(f"Error running agent on task {task_id}: {e}")
         agent_error = f"AGENT ERROR: {e}"
    finally:
        log_entry = {
            "Task ID": task_id,
            "Question": question_text,
            "Submitted Answer": submitted_answer or agent_error,
        }
        return payload, log_entry    
    
def _submit_all(username, agent_code, answers_payload, submit_url):
    # Prepare Submission 
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    status_update = f"Submitting {len(answers_payload)} answers for user '{username}'..."
    print(status_update)

    # Submit Answers
    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(final_status)
        return final_status
    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)
        return status_message
    except requests.exceptions.Timeout:
        status_message = "Submission Failed: The request timed out."
        print(status_message)
        return status_message
    except requests.exceptions.RequestException as e:
        status_message = f"Submission Failed: Network error - {e}"
        print(status_message)
        return status_message
    except Exception as e:
        status_message = f"An unexpected error occurred during submission: {e}"
        print(status_message)
        return status_message

    
def prepare_agent(api_key=None):  
    # 1. Instantiate Agent ( modify this part to create your agent)
    try:
        agent = GeminiAgentContainer(api_key=api_key)
        print(agent.system_prompt)
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return None
    
    return agent

def save_answers_to_file():
    """
    Submits the answers to a local file named with the current epoch time.
    """
    if not answers_by_task:
        return ("Nothing to save, no answers found.")
    answers_payload = list(answers_by_task.values())

    file_path = f"answers-{int(time.time())}.json"
    print(f"Saving answers to file: {file_path}")
    try:
        with open(file_path, "w") as file:
            json.dump(answers_payload, file, indent=4)
        submit_status = (f"Answers successfully written to {file_path}")
    except Exception as e:
        submit_status = (f"Error writing answers to file: {e}")
    print(submit_status)
    return submit_status


def run_all(api_key: str | None = None):
    """
    Fetches all questions, runs the BasicAgent on them,
    """
    
    questions_url = f"{DEFAULT_API_URL}/questions"

    agent = prepare_agent(api_key)

    questions_data = load_questions(questions_url)
    # 3. Run your Agent
    
    print(f"Running agent on {len(questions_data)} questions...")
    for item in questions_data:
        payload_data, log_entry = answer_one(agent, item)
        if payload_data:
            task_id = payload_data.get("task_id")
            answers_by_task[task_id] = payload_data
        results_log.append(log_entry)
        time.sleep(1)
    if not answers_by_task:
        final_status = "Agent did not produce any answers to submit."
    else:
        final_status = f"Agent finished, {len(answers_by_task)} answers produced."
    print(final_status)
    return final_status, pd.DataFrame(results_log)

def submit_all( profile: gr.OAuthProfile | None):
    """
    Submits all answers and displays the results.
    """
    submit_url = f"{DEFAULT_API_URL}/submit"

    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."
    # --- 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 not answers_by_task:
        submit_status = "No answers to submit."
    else:
        # 4. Submit all answers
        # 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"

        submit_status = _submit_all(username, agent_code, list(answers_by_task.values()), submit_url)

    return submit_status


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

        1.  Please use your own Gemini API key to run the agent. You can find your API key in your [Gemini account settings](https://gemini.com/account/settings).
        2.  Click 'Run Evaluation' to fetch questions, run the agent, and see the answers.
        3.  Click 'Submit All Answers' to submit the answers to the server.
       """
    )

    gr.LoginButton()

    api_key_input = gr.Textbox(
        label="Gemini API Key",
        placeholder="Enter your Gemini API key here",
        type="password",
        lines=1,
        visible=True
    )

    run_button = gr.Button("Run Evaluation")
    save_button = gr.Button("Save Answers to File")
    submit_button = gr.Button("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_all,
        inputs=[api_key_input],
        outputs=[status_output, results_table]
    )

    save_button.click(
        fn=save_answers_to_file,
        outputs=[status_output]
    )

    submit_button.click(
        fn=submit_all,
        outputs=[status_output]
    )

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_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup

    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.")
        print(f"API KEY: {os.getenv('GOOGLE_API_KEY')}")

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

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