app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from huggingface_hub import login
from dotenv import load_dotenv

from multi_agent import orchestrate
from config import config

# (Keep Constants as is)
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
QUESTION_FILE_PATH = "data/gaia_validation.jsonl"
QUESTION_LEVEL     = 1

# --- Basic Agent Definition ---
# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")
    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        fixed_answer = "This is a default answer."
        print(f"Agent returning fixed answer: {fixed_answer}")
        return fixed_answer

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)

    # 2. Fetch 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}")
         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
    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
        try:
            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})
        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}"})

    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:
        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("Submission successful.")
        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 test_init_agent_for_chat(question,
                            openai_api_key,
                            gemini_api_key,
                            anthropic_api_key,
                            space_id,
                            hf_token,
                            serper_api_key,
                            file_name
                            ):

    if file_name:
        file_name = f"data/{file_name}"

    if not question:
        raise gr.Error("Question is required.")
    if not openai_api_key:
        raise gr.Error("OpenAi Key is required.")
    if not space_id:
        raise gr.Error("Space Id is required.")
    if not hf_token:
        raise gr.Error("HF Token is required.")

    try:
        os.environ["OPENAI_API_KEY"] = openai_api_key
        os.environ["GEMINI_API_KEY"] = gemini_api_key
        os.environ["ANTHROPIC_API_KEY"] = anthropic_api_key
        os.environ["SPACE_ID"] = space_id
        os.environ["HF_TOKEN"] = hf_token
        os.environ["SERPER_API_KEY"] = serper_api_key

        config.OPENAI_API_KEY = openai_api_key
        config.GEMINI_API_KEY = gemini_api_key
        config.ANTHROPIC_API_KEY = anthropic_api_key
        config.SPACE_ID = space_id
        config.HF_TOKEN = hf_token
        config.SERPER_API_KEY = serper_api_key

        submitted_answer = orchestrate(question, file_name)
    
    except Exception as e:
        raise gr.Error(e)
    # finally:
    #     del os.environ["OPENAI_API_KEY"]
    #     del os.environ["GEMINI_API_KEY"]
    #     del os.environ["ANTHROPIC_API_KEY"]
    #     del os.environ["SPACE_ID"]
    #     del os.environ["HF_TOKEN"]
    #     del os.environ["SERPER_API_KEY"]

    return submitted_answer

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

        1. Who is in the final of champions league in 2025?
        2. Who is in the final of champions league form 2020 to 2025?
        3. What is the colour of the suit in this image: https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2Fimages.hdqwalls.com%2Fwallpapers%2Fblack-superman-henry-cavill-xa.jpg&f=1&nofb=1&ipt=451cdc8bb05635ac59e50dc567cb68ae38ad45a626622ee7760b2c3ef828d5a7?
        4. Which of the fruits shown in the 2008 painting “Embroidery from Uzbekistan” were served as part of the October 1949 breakfast menu for the ocean liner that was later used as a floating prop for the film “The Last Voyage”? Give the items as a comma-separated list, ordering them in clockwise order based on their arrangement in the painting starting from the 12 o’clock position. Use the plural form of each fruit.

        """
    )

    with gr.Row():
        space_id = gr.Textbox(
            label="space Id *",
            type="password",
            placeholder="Dkapsis/assignment-gaia-agent",
            interactive=True
        )
        hf_token = gr.Textbox(
            label="HF Token *",
            type="password",
            placeholder="hf_password",
            interactive=True
        )
        openai_api_key = gr.Textbox(
            label="OpenAI API Key *",
            type="password",
            placeholder="sk‑...",
            interactive=True
        )

    with gr.Row():
        serper_api_key = gr.Textbox(
            label="Serper API Key",
            type="password",
            placeholder="password",
            interactive=True
        )
        gemini_api_key = gr.Textbox(
            label="Gemini API Key",
            type="password",
            interactive=True
        )
        anthropic_api_key = gr.Textbox(
            label="Anthropic API Key",
            type="password",
            placeholder="password",
            interactive=True
        )
    
    with gr.Row():
        question = gr.Textbox(
            label="Question *",
            placeholder="In the 2025 Gradio Agents & MCP Hackathon, what percentage of participants submitted a solution during the last 24 hours?",
            interactive=True
        )
    
    with gr.Row():
        file_name = gr.Textbox(
            label="File Name",
            interactive=True,
            scale=2
        )
    
    with gr.Row():
        answer = gr.Textbox(
                label="Answer",
                lines=1,
                interactive=False
        )

    with gr.Row():
        submit_btn = gr.Button("Submit", variant="primary")

    gr.LoginButton()
    submit_btn.click(
        fn=test_init_agent_for_chat,
        inputs=[question, openai_api_key, gemini_api_key, anthropic_api_key, space_id, hf_token, serper_api_key, file_name],
        outputs=answer
    )
    # gr.ChatInterface(test_init_agent_for_chat(
    #                         question = question,
    #                         openai_api_key = openai_api_key,
    #                         gemini_api_key = gemini_api_key,
    #                         anthropic_api_key = anthropic_api_key,
    #                         space_id = space_id,
    #                         hf_token = hf_token,
    #                         serper_api_key = serper_api_key
    #                     ), type="messages")

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