app.py

import os
import json
import requests
from PIL import Image
import torch
import gradio as gr
from ppt_parser import transfer_to_structure
from transformers import AutoProcessor, Llama4ForConditionalGeneration

# ✅ Hugging Face token
hf_token = os.getenv("HF_TOKEN")
model_id = "meta-llama/Llama-4-Scout-17B-16E-Instruct"

# ✅ Load model & processor
processor = AutoProcessor.from_pretrained(model_id, token=hf_token)
model = Llama4ForConditionalGeneration.from_pretrained(
    model_id,
    token=hf_token,
    attn_implementation="flex_attention",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

# ✅ Global storage
extracted_text = ""
image_paths = []

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()

# ✅ Handle uploaded PPTX
def handle_pptx_upload(pptx_file):
    global extracted_text, image_paths
    tmp_path = pptx_file.name
    parsed_json_str, image_paths = 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."

# ✅ Multimodal Q&A using Scout
def ask_llama(question):
    global extracted_text, image_paths

    if not extracted_text and not image_paths:
        return "Please upload and extract a PPTX first."

    # 🧠 Build multimodal chat messages
    messages = [
        {
            "role": "user",
            "content": [],
        }
    ]

    # Add up to 2 images to prevent OOM
    for path in image_paths[:2]:
        messages[0]["content"].append({"type": "image", "image": Image.open(path)})

    messages[0]["content"].append({
        "type": "text",
        "text": f"{extracted_text}\n\nQuestion: {question}"
    })

    inputs = processor.apply_chat_template(
        messages,
        add_generation_prompt=True,
        tokenize=True,
        return_dict=True,
        return_tensors="pt"
    ).to(model.device)

    outputs = model.generate(**inputs, max_new_tokens=256)

    response = processor.batch_decode(outputs[:, inputs["input_ids"].shape[-1]:])[0]
    return response.strip()

# ✅ Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("## 🧠 Multimodal Llama 4 Scout Study Assistant")

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

    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 4 Scout")
    ai_answer = gr.Textbox(label="🤖 Answer", lines=6)

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

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