app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from groq import Groq

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

# --- Groq Powered Agent Definition ---
# This new agent uses the Groq API to generate answers.
class GroqAgent:
    def __init__(self, api_key):
        """
        Initializes the GroqAgent with the Groq API client.
        """
        print("Initializing GroqAgent...")
        self.client = Groq(api_key=api_key)
        print("GroqAgent initialized successfully.")

    def __call__(self, question: str) -> str:
        """
        This method is called to answer a question using the Groq API.
        """
        print(f"GroqAgent received question (first 50 chars): {question[:50]}...")

        # A system prompt is used to guide the model to provide concise, direct answers,
        # which is ideal for the GAIA benchmark's exact-match scoring.
        system_prompt = (
            "You are an expert AI agent. Your goal is to answer the following question as accurately "
            "and concisely as possible. Provide only the final answer, without any introductory text, "
            "explanations, or additional formatting."
        )

        try:
            chat_completion = self.client.chat.completions.create(
                messages=[
                    {
                        "role": "system",
                        "content": system_prompt,
                    },
                    {
                        "role": "user",
                        "content": question,
                    }
                ],
                model="llama3-70b-8192",  # A powerful model available via Groq
                temperature=0.0, # Set to 0 for deterministic, factual answers
            )

            answer = chat_completion.choices[0].message.content.strip()
            print(f"GroqAgent generated answer: {answer}")
            return answer

        except Exception as e:
            print(f"An error occurred while calling Groq API: {e}")
            return f"Error generating answer: {e}"


def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the GroqAgent 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 (Now using GroqAgent)
    try:
        # Securely get the API key from Hugging Face Space secrets
        groq_api_key = os.getenv("GROQ_API_KEY")
        if not groq_api_key:
            raise ValueError("GROQ_API_KEY secret not found! Please set it in your Space's settings.")
        agent = GroqAgent(api_key=groq_api_key)
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None

    # The link to your codebase (useful for verification, so please keep your space public)
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(f"Agent code link: {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


# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# Groq-Powered Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**
        1.  Make sure you have set your `GROQ_API_KEY` in the 'Secrets' section of your Space settings.
        2.  Log in to your Hugging Face account using the button below. This is required for submission.
        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit your answers, and see your score.
        ---
        **Disclaimers:**
        Once you click the "submit" button, the process can take some time as the agent answers all the questions.
        This space provides a basic setup. You are encouraged to modify the `GroqAgent` class to experiment with different models, prompts, or even add tools to improve your score!
        """
    )

    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)

    # CORRECTED LINE: The `inputs` argument is removed. Gradio passes the
    # OAuthProfile automatically to the `run_and_submit_all` function
    # because of the type hint in its definition.
    run_button.click(
        fn=run_and_submit_all,
        outputs=[status_output, results_table]
    )

if __name__ == "__main__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    space_id_startup = os.getenv("SPACE_ID")
    if space_id_startup:
        print(f"✅ SPACE_ID found: {space_id_startup}")
        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
    else:
        print("ℹ️  SPACE_ID environment variable not found (running locally?).")

    if not os.getenv("GROQ_API_KEY"):
         print("⚠️  WARNING: GROQ_API_KEY secret is not set. The app will fail if run.")
    else:
         print("✅ GROQ_API_KEY secret is set.")


    print("-"*(60 + len(" App Starting ")) + "\n")
    print("Launching Gradio Interface for Groq Agent Evaluation...")
    demo.launch(debug=True, share=False)