app.py

import gradio as gr
from PIL import Image
import json
from byaldi import RAGMultiModalModel
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch
import re

# Load models
def load_models():
    RAG = RAGMultiModalModel.from_pretrained("vidore/colpali")
    model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True, torch_dtype=torch.float32)  # float32 for CPU
    processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True)
    return RAG, model, processor

RAG, model, processor = load_models()

# Global variable to store extracted text
extracted_text_global = ""

# Function for OCR extraction
def extract_text(image):
    global extracted_text_global
    text_query = "Extract all the text in Sanskrit and English from the image."
    
    # Prepare message for Qwen model
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image", "image": image},
                {"type": "text", "text": text_query},
            ],
        }
    ]
    
    # Process the image
    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    image_inputs, video_inputs = process_vision_info(messages)
    inputs = processor(
        text=[text], 
        images=image_inputs, 
        videos=video_inputs, 
        padding=True, 
        return_tensors="pt"
    ).to("cpu")  # Use CPU

    # Generate text
    with torch.no_grad():
        generated_ids = model.generate(**inputs, max_new_tokens=2000)
    
    generated_ids_trimmed = [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
    extracted_text = processor.batch_decode(
        generated_ids_trimmed, 
        skip_special_tokens=True, 
        clean_up_tokenization_spaces=False
    )[0]
    
    # Store extracted text in global variable
    extracted_text_global = extracted_text
    return extracted_text

# Function for keyword search within extracted text
def search_keyword(keyword):
    global extracted_text_global
    if not extracted_text_global:
        return "No extracted text available. Please extract text first.", "No matches found."
    
    keyword_lower = keyword.lower()
    sentences = extracted_text_global.split('. ')
    matched_sentences = []

    # Perform keyword search with highlighting
    for sentence in sentences:
        if keyword_lower in sentence.lower():
            highlighted_sentence = re.sub(
                f'({re.escape(keyword)})', 
                r'<mark>\1</mark>',  # Highlight the matched keyword
                sentence, 
                flags=re.IGNORECASE
            )
            matched_sentences.append(highlighted_sentence)

    search_results_str = "<br>".join(matched_sentences) if matched_sentences else "No matches found."
    return extracted_text_global, search_results_str

# Gradio App
def app_extract(image):
    extracted_text = extract_text(image)
    return extracted_text

def app_search(keyword):
    extracted_text, search_results = search_keyword(keyword)
    return extracted_text, search_results

# Gradio Interface with two buttons
iface = gr.Interface(
    fn=[app_extract, app_search],
    inputs=[
        gr.Image(type="pil", label="Upload an Image"),
        gr.Textbox(label="Enter keyword to search in extracted text", placeholder="Keyword")
    ],
    outputs=[
        gr.Textbox(label="Extracted Text"),
        gr.HTML(label="Search Results"),
    ],
    title="OCR and Keyword Search in Images",
    live=False,
    description="First, extract the text from an image, then search for a keyword in the extracted text.",
    layout="vertical",
    allow_flagging="never"
)

# Create separate buttons
extract_button = gr.Button("Extract Text")
search_button = gr.Button("Search Keyword")

# Link buttons to their respective functions
extract_button.click(fn=app_extract, inputs=[gr.Image(type="pil")], outputs=[gr.Textbox(label="Extracted Text")])
search_button.click(fn=app_search, inputs=[gr.Textbox(label="Enter keyword")], outputs=[gr.Textbox(label="Extracted Text"), gr.HTML(label="Search Results")])

# Launch Gradio App
iface.launch()