app.py

import os
import gradio as gr
import requests
import pandas as pd
from transformers import pipeline
import re

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

# -----------------------------------------
#                BASIC AGENT
# -----------------------------------------

class BasicAgent:
    def __init__(self):
        print("Loading lightweight GAIA agent model...")

        # Lightweight model for HF CPU Spaces (stable)
        self.generator = pipeline(
            "text-generation",
            model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
            max_new_tokens=32,
            do_sample=False,
            temperature=0.0,
        )

        print("Model loaded successfully.")

    # -------------------------
    # TOOL 1: Reverse text
    # -------------------------
    def try_reverse(self, question: str):
        q = question.strip()

        # Only reverse if clearly reversed (starts with dot)
        if q.startswith("."):
            return q[::-1]

        return None

    # -------------------------
    # TOOL 2: Safe arithmetic
    # -------------------------
    def try_math(self, question: str):
        try:
            pattern = r"\d+\.?\d*\s*[\+\-\*\/]\s*\d+\.?\d*"
            match = re.search(pattern, question)
            if match:
                expression = match.group()
                result = eval(expression)

                if float(result).is_integer():
                    return str(int(result))
                return str(result)
        except:
            pass

        return None

    # -------------------------
    # STRICT CLEANING (Exact Match)
    # -------------------------
    def clean_answer(self, text: str) -> str:
        text = text.strip()

        if "Answer:" in text:
            text = text.split("Answer:")[-1]

        text = text.split("\n")[0].strip()

        # Remove quotes and trailing punctuation
        text = text.strip('"').strip("'")
        text = re.sub(r"[\.]$", "", text)

        return text.strip()

    # -------------------------
    # MODEL CALL
    # -------------------------
    def ask_model(self, question: str):
        prompt = f"""You are answering a benchmark question.
Return ONLY the exact final answer.
No explanation.
No extra words.
If number → return number only.
If word → return word only.

Question: {question}
Answer:"""

        output = self.generator(prompt)[0]["generated_text"]
        answer = output.replace(prompt, "")

        return self.clean_answer(answer)

    # -------------------------
    # MAIN LOGIC
    # -------------------------
    def __call__(self, question: str) -> str:
        print(f"Processing: {question[:60]}...")

        # 1️⃣ Reverse tool
        reversed_q = self.try_reverse(question)
        if reversed_q:
            print("Used reverse tool.")
            return self.ask_model(reversed_q)

        # 2️⃣ Math tool
        math_result = self.try_math(question)
        if math_result:
            print("Used math tool.")
            return math_result

        # 3️⃣ LLM reasoning
        answer = self.ask_model(question)

        # Retry once if output too long
        if len(answer.split()) > 5:
            print("Retrying for shorter answer...")
            answer = self.ask_model(question)

        print(f"Final Answer: {answer}")
        return answer


# -----------------------------------------
#         RUN + SUBMIT FUNCTION
# -----------------------------------------

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

    space_id = os.getenv("SPACE_ID")

    if profile:
        username = profile.username
        print(f"User logged in: {username}")
    else:
        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"

    # Instantiate agent
    try:
        agent = BasicAgent()
    except Exception as e:
        return f"Error initializing agent: {e}", None

    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"

    # Fetch Questions
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
    except Exception as e:
        return f"Error fetching questions: {e}", None

    results_log = []
    answers_payload = []

    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:
            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:
            results_log.append(
                {
                    "Task ID": task_id,
                    "Question": question_text,
                    "Submitted Answer": f"AGENT ERROR: {e}",
                }
            )

    if not answers_payload:
        return "Agent did not produce any answers.", pd.DataFrame(results_log)

    submission_data = {
        "username": username.strip(),
        "agent_code": agent_code,
        "answers": answers_payload,
    }

    # Submit
    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', '?')}/"
            f"{result_data.get('total_attempted', '?')} correct)\n"
            f"Message: {result_data.get('message', '')}"
        )

        return final_status, pd.DataFrame(results_log)

    except Exception as e:
        return f"Submission Failed: {e}", pd.DataFrame(results_log)



with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")

    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
    )

    run_button.click(
        fn=run_and_submit_all,
        outputs=[status_output, results_table],
    )

if __name__ == "__main__":
    demo.launch(debug=True, share=False)