app.py

import os
import gradio as gr
from transformers import ViTFeatureExtractor, ViTModel
from PIL import Image
from transformers import AutoTokenizer, AutoModel
import torch
from pdf2image import convert_from_path
import io
from io import BytesIO

# CSS styles
css = """
.button { 
    padding: 10px 20px; 
    background: #007BFF; 
    color: white; 
    border: none; 
    cursor: pointer; 
    font-size: 16px; 
    margin: 10px;
}
"""

# Define layout with custom styles
layout = [
    gr.Row([gr.File(label="Upload PDF", type="binary")]),  # Corrected 'type' parameter
    gr.Row([gr.Button("Generate Insights")]),
    gr.Row([gr.Textbox("Placeholder for PDF insights", label="Insights", type="text")])
]

# Function to get image embeddings using ViT
def get_image_embeddings(image_path, model_name='google/vit-base-patch16-224'):
    feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)
    model = ViTModel.from_pretrained(model_name)
    
    image = Image.open(image_path)
    inputs = feature_extractor(images=image, return_tensors="pt")
    outputs = model(**inputs)
    embeddings = outputs.last_hidden_state.mean(dim=1)  # Mean pooling
    return embeddings

# Function to convert PDF to images
def pdf_to_images(pdf_file, img_dir):
    images = convert_from_path(pdf_file)
    
    # Create the directory if it doesn't exist
    os.makedirs(img_dir, exist_ok=True)

    for i, image in enumerate(images):
        image_path = f"{img_dir}/page_{i + 1}.png"
        image.save(image_path, "PNG")

    print(f"Converted {len(images)} pages to images and saved in {img_dir}")

# Function to get text embeddings using a transformer model
def get_text_embeddings(text, model_name='bert-base-uncased'):
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModel.from_pretrained(model_name)
    
    inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=512)
    outputs = model(**inputs)
    embeddings = outputs.last_hidden_state.mean(dim=1)  # Mean pooling
    return embeddings

# Function to process PDF and generate a response
def process_pdf_and_generate_response(pdf_file):
    try:
        # Convert the binary stream to a file-like object
        pdf_file_stream = BytesIO(pdf_file)

        # Convert PDF to images
        img_dir = "pdf_images"
        pdf_to_images(pdf_file_stream, img_dir)

        # Generate embeddings for each image
        image_embeddings = []
        for filename in os.listdir(img_dir):
            if filename.endswith(".png"):
                image_path = os.path.join(img_dir, filename)
                image_embeddings.append(get_image_embeddings(image_path))

        # Perform some text analysis on the PDF content (replace with your logic)
        pdf_text = "PDF content analysis placeholder"
        text_embeddings = get_text_embeddings(pdf_text)

        # Combine image and text embeddings and generate a response (replace with your logic)
        combined_embeddings = torch.cat([*image_embeddings, text_embeddings], dim=0)
        response = "Response based on the processed PDF"
    except Exception as e:
        response = f"An error occurred: {str(e)}"
    return response

iface = gr.Interface(
    fn=process_pdf_and_generate_response,
    inputs=gr.File(label="Upload PDF", type="binary"),  # Corrected 'type' parameter
    outputs=gr.Textbox("Placeholder for PDF insights", label="Insights", type="text"),
    title="pdf-chatbot",
    description="Upload a PDF and receive insights based on its content.",
    css=css
)

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