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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
# 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()
# Function for OCR and search
def ocr_and_search(image, keyword):
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]
# Save extracted text to JSON
output_json = {"query": text_query, "extracted_text": extracted_text}
# json_output = json.dumps(output_json, ensure_ascii=False, indent=4)
gr.Textbox(label= extracted_text)
# Perform keyword search
keyword_lower = keyword.lower()
sentences = extracted_text.split('. ')
matched_sentences = [sentence for sentence in sentences if keyword_lower in sentence.lower()]
gr.Textbox(label= matched_sentences)
return extracted_text, matched_sentences #, json_output
# Gradio App
def app(image, keyword):
extracted_text, search_results = ocr_and_search(image, keyword)
search_results_str = "\n".join(search_results) if search_results else "No matches found."
return extracted_text, search_results_str #, json_output
# Gradio Interface
iface = gr.Interface(
fn=app,
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.Textbox(label="Search Results"),
# gr.JSON(label="JSON Output")
],
title="OCR and Keyword Search in Images",
)
# Launch Gradio App
iface.launch() |