from transformers import MllamaForConditionalGeneration, AutoProcessor, TextIteratorStreamer
from PIL import Image
import requests
import torch
from threading import Thread
import gradio as gr
from gradio import FileData
import time
import spaces
from pdf2image import convert_from_path
import os
from PyPDF2 import PdfReader
import tempfile

ckpt = "Daemontatox/DocumentCogito"
model = MllamaForConditionalGeneration.from_pretrained(ckpt,
    torch_dtype=torch.bfloat16).to("cuda")
processor = AutoProcessor.from_pretrained(ckpt)

def process_pdf(pdf_path):
    """Convert PDF pages to images and extract text."""
    images = convert_from_path(pdf_path)
    pdf_reader = PdfReader(pdf_path)
    text = ""
    for page in pdf_reader.pages:
        text += page.extract_text() + "\n"
    return images, text

def is_pdf(file_path):
    """Check if the file is a PDF."""
    return file_path.lower().endswith('.pdf')

@spaces.GPU()
def bot_streaming(message, history, max_new_tokens=2048):
    txt = message["text"]
    ext_buffer = f"{txt}"
    
    messages = []
    images = []
    
    # Process history
    for i, msg in enumerate(history):
        if isinstance(msg[0], tuple):
            messages.append({"role": "user", "content": [{"type": "text", "text": history[i+1][0]}, {"type": "text", "text": history[i+1][1]}]})
            messages.append({"role": "assistant", "content": [{"type": "text", "text": history[i+1][1]}]})
            images.append(Image.open(msg[0][0]).convert("RGB"))
        elif isinstance(history[i-1], tuple) and isinstance(msg[0], str):
            pass
        elif isinstance(history[i-1][0], str) and isinstance(msg[0], str):
            messages.append({"role": "user", "content": [{"type": "text", "text": msg[0]}]})
            messages.append({"role": "assistant", "content": [{"type": "text", "text": msg[1]}]})

    # Process current message
    if len(message["files"]) == 1:
        file_path = message["files"][0]["path"] if isinstance(message["files"][0], dict) else message["files"][0]
        
        if is_pdf(file_path):
            # Handle PDF
            pdf_images, pdf_text = process_pdf(file_path)
            images.extend(pdf_images)
            txt = f"{txt}\nExtracted text from PDF:\n{pdf_text}"
        else:
            # Handle regular image
            image = Image.open(file_path).convert("RGB")
            images.append(image)
            
        messages.append({"role": "user", "content": [{"type": "text", "text": txt}, {"type": "image"}]})
    else:
        messages.append({"role": "user", "content": [{"type": "text", "text": txt}]})

    texts = processor.apply_chat_template(messages, add_generation_prompt=True)

    if not images:
        inputs = processor(text=texts, return_tensors="pt").to("cuda")
    else:
        inputs = processor(text=texts, images=images, return_tensors="pt").to("cuda")
    
    streamer = TextIteratorStreamer(processor, skip_special_tokens=True, skip_prompt=True)
    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=max_new_tokens)
    generated_text = ""
    
    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()
    buffer = ""
    
    for new_text in streamer:
        buffer += new_text
        generated_text_without_prompt = buffer
        time.sleep(0.01)
        yield buffer

demo = gr.ChatInterface(
    fn=bot_streaming,
    title="Document Analyzer",
    examples=[
        [{"text": "Which era does this piece belong to? Give details about the era.", "files":["./examples/rococo.jpg"]}, 200],
        [{"text": "Where do the droughts happen according to this diagram?", "files":["./examples/weather_events.png"]}, 250],
        [{"text": "What happens when you take out white cat from this chain?", "files":["./examples/ai2d_test.jpg"]}, 250],
        [{"text": "How long does it take from invoice date to due date? Be short and concise.", "files":["./examples/invoice.png"]}, 250],
        [{"text": "Where to find this monument? Can you give me other recommendations around the area?", "files":["./examples/wat_arun.jpg"]}, 250],
    ],
    textbox=gr.MultimodalTextbox(),
    additional_inputs=[
        gr.Slider(
            minimum=10,
            maximum=500,
            value=2048,
            step=10,
            label="Maximum number of new tokens to generate",
        )
    ],
    cache_examples=False,
    description="MllM Document and PDF Analyzer",
    stop_btn="Stop Generation",
    fill_height=True,
    multimodal=True
)

# Update file types to include PDFs
demo.textbox.file_types = ["image", "pdf"]

demo.launch(debug=True)