import os
import gradio as gr
from transformers import ViTFeatureExtractor, ViTModel
from PIL import Image
from transformers import AutoTokenizer, AutoModel
import torch

# 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
from pdf2image import convert_from_path

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):
    # Convert PDF to images
    img_dir = "pdf_images"
    pdf_to_images(pdf_file, 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"
    return response

# Gradio interface
iface = gr.Interface(
    fn=process_pdf_and_generate_response,
    inputs=gr.inputs.File(label="Upload PDF", type="file"),
    outputs=gr.outputs.Textbox(),
    title="Talk2Deck - Interact with your PDFs",
    description="Upload a PDF and receive insights based on its content."
)

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