import os
import re
import requests
import gradio as gr
from PIL import Image
from pypdf import PdfReader
from smolagents import CodeAgent, DuckDuckGoSearchTool, InferenceClientModel, tool

# ১. মডেল সেটআপ (Lightweight & Fast)
HF_TOKEN = os.getenv("HF_TOKEN")
model = InferenceClientModel(
    model_id="Qwen/Qwen2.5-Coder-1.5B-Instruct",
    token=HF_TOKEN
)

# ২. কাস্টম ফাইল রিডার টুলস
@tool
def read_pdf_content(file_path: str) -> str:
    """Reads and extracts text from a PDF file.
    
    Args:
        file_path: Path to the PDF file.
    """
    try:
        reader = PdfReader(file_path)
        text = ""
        for p in reader.pages:
            text += p.extract_text() or ""
        return text[:3000] if text else "No text found."
    except Exception as e:
        return f"Error reading PDF: {e}"

@tool
def inspect_image(file_path: str) -> str:
    """Inspects image dimensions and metadata.
    
    Args:
        file_path: Path to the image.
    """
    try:
        with Image.open(file_path) as img:
            return f"Format: {img.format}, Size: {img.size}, Mode: {img.mode}"
    except Exception as e:
        return f"Error reading image: {e}"

# ৩. এজেন্ট তৈরি
gaia_agent = CodeAgent(
    tools=[DuckDuckGoSearchTool(), read_pdf_content, inspect_image],
    model=model,
    max_steps=5,
    additional_authorized_imports=["math", "statistics", "re", "json", "datetime"]
)

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

def solve_single_task(question: str, file_path: str = None) -> str:
    prompt = f"Question: {question}\n"
    if file_path:
        prompt += f"Local file available at: {file_path}\n"
    prompt += "Provide ONLY the concise final answer without explanations, reasoning, or 'FINAL ANSWER' prefix."
    try:
        raw = str(gaia_agent.run(prompt)).strip()
        return re.sub(r'^(FINAL ANSWER|Answer)[:\s]*', '', raw, flags=re.IGNORECASE).strip()
    except Exception:
        return "N/A"

def run_and_submit_all(profile: gr.OAuthProfile | None):
    if not profile:
        return "Please Login to Hugging Face with the button first.", None

    username = profile.username
    space_id = os.getenv("SPACE_ID", "Learnix-AI-Lab/Final_Assignment_Template")
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    
    questions_url = f"{DEFAULT_API_URL}/questions"
    submit_url = f"{DEFAULT_API_URL}/submit"

    try:
        resp = requests.get(questions_url, timeout=20)
        resp.raise_for_status()
        questions_data = resp.json()
    except Exception as e:
        return f"Error fetching questions: {e}", None

    answers_payload = []
    display_rows = []

    for item in questions_data:
        task_id = item.get("task_id")
        q_text = item.get("question")
        file_name = item.get("file_name")
        local_path = None

        if file_name:
            file_dl_url = f"{DEFAULT_API_URL}/files/{task_id}"
            dl_resp = requests.get(file_dl_url)
            if dl_resp.status_code == 200:
                local_path = f"tmp_{file_name}"
                with open(local_path, "wb") as f:
                    f.write(dl_resp.content)

        ans = solve_single_task(q_text, local_path)
        answers_payload.append({"task_id": task_id, "submitted_answer": ans})
        display_rows.append([q_text, ans])

        if local_path and os.path.exists(local_path):
            os.remove(local_path)

    submit_data = {
        "username": username,
        "agent_code": agent_code,
        "answers": answers_payload
    }
    
    try:
        sub_resp = requests.post(submit_url, json=submit_data, timeout=30)
        sub_resp.raise_for_status()
        result_json = sub_resp.json()
        score = result_json.get("score", "N/A")
        return f"Successfully submitted! Your Score: {score}", display_rows
    except Exception as e:
        return f"Submission error: {e}", display_rows

# ৪. Gradio ইন্টারফেস
with gr.Blocks() as demo:
    gr.Markdown("# 🤖 GAIA Benchmark Evaluator")
    gr.LoginButton()
    run_btn = gr.Button("Run Evaluation & Submit All Answers", variant="primary")
    status_box = gr.Textbox(label="Status / Result", lines=3)
    results_table = gr.DataFrame(headers=["Question", "Agent Answer"], label="Answers")

    run_btn.click(
        fn=run_and_submit_all,
        outputs=[status_box, results_table]
    )

if __name__ == "__main__":
    demo.launch()