app.py

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
import fitz  # PyMuPDF
import io
import numpy as np

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

# Document state to track uploaded files
class DocumentState:
    def __init__(self):
        self.current_doc_images = []
        self.current_doc_text = ""
        self.doc_type = None
        
    def clear(self):
        self.current_doc_images = []
        self.current_doc_text = ""
        self.doc_type = None
        
doc_state = DocumentState()

# Function to convert PDF to images and extract text
def process_pdf_file(file_path):
    """Convert PDF to images and extract text using PyMuPDF."""
    doc = fitz.open(file_path)
    images = []
    text = ""
    
    # Process each page
    for page_num in range(doc.page_count):
        page = doc[page_num]
        text += f"Page {page_num + 1} content:\n{page.get_text()}\n"
        pix = page.get_pixmap(matrix=fitz.Matrix(300/72, 300/72))
        img_data = pix.tobytes("png")
        img = Image.open(io.BytesIO(img_data))
        images.append(img.convert("RGB"))
    
    doc.close()
    return images, text

# Function to process uploaded files (PDF or image)
def process_file(file):
    """Process either PDF or image file and update document state."""
    doc_state.clear()
    
    if isinstance(file, dict):
        file_path = file["path"]
    else:
        file_path = file
        
    if file_path.lower().endswith('.pdf'):
        doc_state.doc_type = 'pdf'
        doc_state.current_doc_images, doc_state.current_doc_text = process_pdf_file(file_path)
        return f"PDF processed. Total pages: {len(doc_state.current_doc_images)}. You can now ask questions about the content."
    else:
        doc_state.doc_type = 'image'
        doc_state.current_doc_images = [Image.open(file_path).convert("RGB")]
        return "Image loaded successfully. You can now ask questions about the content."

# Function to handle streaming responses from the model
@spaces.GPU()
def bot_streaming(message, history, max_new_tokens=8192):
    txt = message["text"]
    messages = []
    
    # Process new file if provided
    if message.get("files") and len(message["files"]) > 0:
        process_file(message["files"][0])
    
    # Process history
    for i, msg in enumerate(history):
        if isinstance(msg[0], dict):  # Multimodal message (text + files)
            user_content = [{"type": "text", "text": msg[0]["text"]}]
            if "files" in msg[0] and len(msg[0]["files"]) > 0:
                user_content.append({"type": "image"})
            messages.append({"role": "user", "content": user_content})
            messages.append({"role": "assistant", "content": [{"type": "text", "text": msg[1]}]})
        elif isinstance(msg[0], str):  # Text-only message
            messages.append({"role": "user", "content": [{"type": "text", "text": msg[0]}]})
            messages.append({"role": "assistant", "content": [{"type": "text", "text": msg[1]}]})

    # Include document context in the current message
    if doc_state.current_doc_images:
        context = f"\nDocument context:\n{doc_state.current_doc_text}" if doc_state.current_doc_text else ""
        current_msg = f"{txt}{context}"
        messages.append({"role": "user", "content": [{"type": "text", "text": current_msg}, {"type": "image"}]})
    else:
        messages.append({"role": "user", "content": [{"type": "text", "text": txt}]})

    # Apply chat template to messages
    texts = processor.apply_chat_template(messages, add_generation_prompt=True)
    
    # Process inputs based on whether we have images
    if doc_state.current_doc_images:
        inputs = processor(
            text=texts,
            images=doc_state.current_doc_images[0:1],  # Only use first image
            return_tensors="pt"
        ).to("cuda")
    else:
        inputs = processor(text=texts, 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)
    
    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()
    buffer = ""
    
    for new_text in streamer:
        buffer += new_text
        time.sleep(0.01)
        yield buffer

# Function to clear document context
def clear_context():
    """Clear the current document context."""
    doc_state.clear()
    return "Document context cleared. You can upload a new document."

# Create the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Document Analyzer with Chat Support")
    gr.Markdown("Upload a PDF or image and chat about its contents. For PDFs, all pages will be processed for visual analysis.")
    
    chatbot = gr.ChatInterface(
        fn=bot_streaming,
        title="Document Chat",
        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=2048,
                value=8192,
                step=10,
                label="Maximum number of new tokens to generate",
            )
        ],
        cache_examples=False,
        stop_btn="Stop Generation",
        fill_height=True,
        multimodal=True
    )
    
    clear_btn = gr.Button("Clear Document Context")
    clear_btn.click(fn=clear_context)
    
    # Update accepted file types
    chatbot.textbox.file_types = ["image", "pdf","text"]

# Launch the interface
demo.launch(debug=True)