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import gradio as gr
from PIL import Image
from byaldi import RAGMultiModalModel
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch
# Load models colpali
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 Hindi and English from the image."
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": image},
{"type": "text", "text": text_query},
],
}
]
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")
# 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)]
# Decode output while avoiding any coordinate information
extracted_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=True
)[0]
extracted_text = extracted_text.replace("The text in the image is:", "").strip()
# Filter out any unwanted text (like coordinates)
extracted_text = ' '.join(filter(lambda x: not any(char.isdigit() for char in x), extracted_text.split()))
# Separate English and Hindi text using a simple heuristic
english_text = ' '.join(filter(lambda x: all((char.islower() or char.isupper()) or char == "'" for char in x), extracted_text.split()))
hindi_text = ' '.join(filter(lambda x: any(ord(char) >= 128 for char in x), extracted_text.split()))
# Perform keyword search
keyword_lower = keyword.lower().strip()
matched_keywords = []
if keyword_lower:
if keyword_lower in extracted_text.lower():
matched_keywords = [keyword]
# Prepare plain text output
plain_text_output = (
f"- English: {' '.join(english_text.split()) if english_text else 'No English text found.'}\n\n"
f"- Hindi: {' '.join(hindi_text.split()) if hindi_text else 'No Hindi text found.'}"
)
return extracted_text, matched_keywords, plain_text_output
# Gradio App function
def app(image, keyword):
# Call OCR and search function
extracted_text, matched_keywords, plain_text_output = ocr_and_search(image, keyword)
# Format search results
search_results_str = "\n".join(matched_keywords) if matched_keywords else "No matches found for the keyword."
return extracted_text, search_results_str, plain_text_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.Textbox(label="Plain Text Output", lines=10) # For plain text output
],
title="Optical Character Recognition with Keyword Search from Images",
)
# Launch Gradio App
iface.launch() |