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
import logging

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

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

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()

def process_pdf_file(file_path):
    """Convert PDF to images and extract text using PyMuPDF."""
    try:
        doc = fitz.open(file_path)
        images = []
        text = ""
        
        for page_num in range(doc.page_count):
            try:
                page = doc[page_num]
                page_text = page.get_text("text")
                if page_text.strip():
                    text += f"Page {page_num + 1}:\n{page_text}\n\n"
                
                zoom = 2
                mat = fitz.Matrix(zoom, zoom)
                pix = page.get_pixmap(matrix=mat, alpha=False)
                img_data = pix.tobytes("png")
                img = Image.open(io.BytesIO(img_data))
                img = img.convert("RGB")
                
                max_size = 1600
                if max(img.size) > max_size:
                    ratio = max_size / max(img.size)
                    new_size = tuple(int(dim * ratio) for dim in img.size)
                    img = img.resize(new_size, Image.Resampling.LANCZOS)
                
                images.append(img)
                
            except Exception as e:
                logger.error(f"Error processing page {page_num}: {str(e)}")
                continue
                
        doc.close()
        
        if not images:
            raise ValueError("No valid images could be extracted from the PDF")
            
        return images, text
        
    except Exception as e:
        logger.error(f"Error processing PDF file: {str(e)}")
        raise

def process_uploaded_file(file):
    """Process uploaded file and update document state."""
    try:
        doc_state.clear()
        
        if file is None:
            return "No file uploaded. Please upload a file."
        
        # Get the file path and extension
        if isinstance(file, dict):
            file_path = file["name"]
        else:
            file_path = file.name
            
        # Get file extension
        file_ext = file_path.lower().split('.')[-1]
        
        # Define allowed extensions
        image_extensions = {'png', 'jpg', 'jpeg', 'gif', 'bmp', 'webp'}
        
        if file_ext == 'pdf':
            doc_state.doc_type = 'pdf'
            try:
                doc_state.current_doc_images, doc_state.current_doc_text = process_pdf_file(file_path)
                return f"PDF processed successfully. Total pages: {len(doc_state.current_doc_images)}. You can now ask questions about the content."
            except Exception as e:
                return f"Error processing PDF: {str(e)}. Please try a different PDF file."
        elif file_ext in image_extensions:
            doc_state.doc_type = 'image'
            try:
                img = Image.open(file_path).convert("RGB")
                max_size = 1600
                if max(img.size) > max_size:
                    ratio = max_size / max(img.size)
                    new_size = tuple(int(dim * ratio) for dim in img.size)
                    img = img.resize(new_size, Image.Resampling.LANCZOS)
                doc_state.current_doc_images = [img]
                return "Image loaded successfully. You can now ask questions about the content."
            except Exception as e:
                return f"Error processing image: {str(e)}. Please try a different image file."
        else:
            return f"Unsupported file type: {file_ext}. Please upload a PDF or image file (PNG, JPG, JPEG, GIF, BMP, WEBP)."
    except Exception as e:
        logger.error(f"Error in process_file: {str(e)}")
        return "An error occurred while processing the file. Please try again."

@spaces.GPU()
def bot_streaming(message, history, max_new_tokens=8192):
    try:
        messages = []
        
        # Process history
        for i, msg in enumerate(history):
            try:
                messages.append({"role": "user", "content": [{"type": "text", "text": msg[0]}]})
                messages.append({"role": "assistant", "content": [{"type": "text", "text": msg[1]}]})
            except Exception as e:
                logger.error(f"Error processing history message {i}: {str(e)}")
                continue

        # Include document context
        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"{message}{context}"
            messages.append({"role": "user", "content": [{"type": "text", "text": current_msg}, {"type": "image"}]})
        else:
            messages.append({"role": "user", "content": [{"type": "text", "text": message}]})

        # Process inputs
        texts = processor.apply_chat_template(messages, add_generation_prompt=True)
        
        try:
            if doc_state.current_doc_images:
                inputs = processor(
                    text=texts,
                    images=doc_state.current_doc_images[0:1],
                    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
                
        except Exception as e:
            logger.error(f"Error in model processing: {str(e)}")
            yield "An error occurred while processing your request. Please try again."
            
    except Exception as e:
        logger.error(f"Error in bot_streaming: {str(e)}")
        yield "An error occurred. Please try again."

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 (PNG, JPG, JPEG, GIF, BMP, WEBP) and chat about its contents.")
    
    with gr.Row():
        file_upload = gr.File(
            label="Upload Document",
            file_types=[".pdf", ".png", ".jpg", ".jpeg", ".gif", ".bmp", ".webp"]
        )
        upload_status = gr.Textbox(
            label="Upload Status",
            interactive=False
        )
    
    clear_btn = gr.Button("Clear Document Context")
    
    chatbot = gr.ChatInterface(
        fn=bot_streaming,
        title="Document Chat",
        additional_inputs=[
            gr.Slider(
                minimum=10,
                maximum=2048,
                value=8192,
                step=10,
                label="Maximum number of new tokens to generate",
            )
        ],
        stop_btn="Stop Generation",
        fill_height=True
    )
    
    file_upload.change(
        fn=process_uploaded_file,
        inputs=[file_upload],
        outputs=[upload_status]
    )
    
    clear_btn.click(
        fn=clear_context,
        outputs=[upload_status]
    )

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