app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
import random
from huggingface_hub import notebook_login
from transformers import Tool
from tools.tool_math import SolveEquationTool, ExplainSolutionTool

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

# --- Math Solver Agent Definition ---
class MathSolverAgent:
    def __init__(self):
        self.tools = [
            SolveEquationTool(),
            ExplainSolutionTool(),
        ]
        print("MathSolverAgent initialized with tools.")

    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        for tool in self.tools:
            if tool.name in question.lower():
                return tool(question)

        try:
            if random.random() < 0.5:
                raise ValueError("Simulating incorrect or skipped answer.")

            solution = self.tools[0](question)
            if solution.startswith("Error"):
                return solution

            explanation = self.tools[1](question)
            return f"{solution}\nExplanation:\n{explanation}"
        except Exception as e:
            return "Sorry, I couldn't solve that one."


def run_and_submit_all(profile: gr.OAuthProfile | None):
    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 = MathSolverAgent()
    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)

    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

    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)

    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)

    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.RequestException as e:
        status_message = f"Submission Failed: {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df

# --- Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# Math Solver and Explainer Agent")
    gr.Markdown(
        """
        **Instructions:**

        1.  Modify this agent to use symbolic math tools and explanations.
        2.  Log in to your Hugging Face account.
        3.  Run the agent and submit all answers for scoring.
        """
    )

    gr.LoginButton()
    
    # manual input
    manual_input = gr.Textbox(label="Try the Agent Manually", placeholder="e.g., Solve for x: 2*x + 3 = 7")
    manual_output = gr.Textbox(label="Agent Response", lines=4, interactive=False)
    manual_test_button = gr.Button("Run Agent Locally")

    def run_manual_input(user_input):
        agent = MathSolverAgent()
        user_input = user_input.strip()
        print(f"Manual input received: {user_input}")
        return agent(user_input)
        #agent = MathSolverAgent()
        #return agent(user_input)

    manual_test_button.click(fn=run_manual_input, inputs=manual_input, outputs=manual_output)


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

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