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Browse files- app.py +110 -0
- requirements.txt +5 -0
app.py
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import gradio as gr
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from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
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from PIL import Image
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import torch
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import re
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# Load the pre-trained model and processor
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model = Qwen2VLForConditionalGeneration.from_pretrained(
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"Qwen/Qwen2-VL-2B-Instruct",
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torch_dtype="auto",
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device_map="auto",
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)
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processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
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# Function to extract text from the image
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def extract_text(image):
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image"},
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{"type": "text", "text": "can u extract the text in hindi"}
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]
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}
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]
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# Process input image and text prompt
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text_prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
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inputs = processor(
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text=[text_prompt],
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images=[image],
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padding=True,
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return_tensors="pt"
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)
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inputs = inputs.to("cuda" if torch.cuda.is_available() else "cpu")
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# Generate output text from the model
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output_ids = model.generate(**inputs, max_new_tokens=1024)
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generated_ids = [
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output_ids[len(input_ids):]
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for input_ids, output_ids in zip(inputs.input_ids, output_ids)
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]
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# Decode the generated text
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extracted_text = processor.batch_decode(
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generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
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)[0] # Extracted text
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return extracted_text
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# Function to highlight keywords in the text, even for right-to-left scripts like Hindi
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def highlight_keywords(extracted_text, keywords):
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highlighted_text = extracted_text
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if keywords:
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for keyword in keywords.split(","):
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keyword = keyword.strip()
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if keyword:
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# Ensure correct Unicode support for keywords (use re.UNICODE for non-ASCII)
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highlighted_text = re.sub(
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re.escape(keyword), # Use re.escape to handle special characters in keywords
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r'<mark>\g<0></mark>', # Highlight the found keyword
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highlighted_text,
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flags=re.IGNORECASE | re.UNICODE # Ignore case, and handle Unicode characters
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)
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return highlighted_text
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# First step: Extract text from the uploaded image
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def extract_text_step(image):
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extracted_text = extract_text(image)
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return extracted_text, extracted_text # Return extracted text and store it in state
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# Second step: Search and highlight keywords in the extracted text
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def highlight_keywords_step(extracted_text, keywords):
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highlighted_text = highlight_keywords(extracted_text, keywords)
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return highlighted_text
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# Gradio UI
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with gr.Blocks() as demo:
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# Step 1: Image Upload and Text Extraction
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with gr.Row():
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image_input = gr.Image(type="pil", label="Upload Image")
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extract_button = gr.Button("Extract Text")
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extracted_text_output = gr.Textbox(label="Extracted Text")
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# Step 2: Keyword Input and Highlighting
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with gr.Row():
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keyword_input = gr.Textbox(label="Enter keywords (comma-separated)", placeholder="Enter keywords after text extraction")
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search_button = gr.Button("Highlight Keywords")
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highlighted_text_output = gr.HTML(label="Highlighted Text with Matches")
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# Define interactions
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extract_button.click(
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fn=extract_text_step, # Call text extraction function
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inputs=image_input,
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outputs=[extracted_text_output, extracted_text_output], # Display text and store in state
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)
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search_button.click(
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fn=highlight_keywords_step, # Call keyword highlighting function
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inputs=[extracted_text_output, keyword_input], # Use extracted text and keywords
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outputs=highlighted_text_output, # Display highlighted text
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)
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# Launch the app
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demo.launch()
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requirements.txt
ADDED
@@ -0,0 +1,5 @@
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git+https://github.com/huggingface/transformers
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gradio
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torch
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pillow
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accelerate>=0.26.0
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