import os
import json
import torch
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from ppt_parser import transfer_to_structure

# ✅ Hugging Face token (optional if public + unauthenticated)
hf_token = os.getenv("HF_TOKEN", None)
model_id = "meta-llama/Llama-3.1-8B-Instruct"

# ✅ Load model + tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id, token=hf_token)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    token=hf_token,
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    device_map="auto"
)
llama_pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512)

# ✅ Global storage
extracted_text = ""

def extract_text_from_pptx_json(parsed_json: dict) -> str:
    text = ""
    for slide in parsed_json.values():
        for shape in slide.values():
            if shape.get("type") == "group":
                for group_shape in shape.get("group_content", {}).values():
                    if group_shape.get("type") == "text":
                        for para_key, para in group_shape.items():
                            if para_key.startswith("paragraph_"):
                                text += para.get("text", "") + "\n"
            elif shape.get("type") == "text":
                for para_key, para in shape.items():
                    if para_key.startswith("paragraph_"):
                        text += para.get("text", "") + "\n"
    return text.strip()

def handle_pptx_upload(pptx_file):
    global extracted_text
    tmp_path = pptx_file.name
    parsed_json_str, _ = transfer_to_structure(tmp_path, "images")
    parsed_json = json.loads(parsed_json_str)
    extracted_text = extract_text_from_pptx_json(parsed_json)
    return extracted_text or "No readable text found in slides."

def ask_llama(question):
    global extracted_text
    if not extracted_text:
        return "Please upload a PPTX file first."

    prompt = f"<|user|>\nContext:\n{extracted_text}\n\nQuestion: {question}<|end|>\n<|assistant|>\n"
    response = llama_pipe(prompt)[0]["generated_text"]
    return response.replace(prompt, "").strip()

# ✅ Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("## 🧠 Study Assistant with LLaMA 3.1 8B")

    pptx_input = gr.File(label="📂 Upload PPTX File", file_types=[".pptx"])
    extract_btn = gr.Button("📜 Extract Slide Text")

    extracted_output = gr.Textbox(label="📄 Slide Text", lines=10, interactive=False)
    extract_btn.click(handle_pptx_upload, inputs=[pptx_input], outputs=[extracted_output])

    question = gr.Textbox(label="❓ Ask a Question")
    ask_btn = gr.Button("💬 Ask LLaMA")
    ai_answer = gr.Textbox(label="🤖 LLaMA Answer", lines=6)

    ask_btn.click(ask_llama, inputs=[question], outputs=[ai_answer])

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