app.py

app.py

@@ -0,0 +1,102 @@
+import re
+import io
+import torch
+import gradio as gr
+from PIL import Image
+from qwen_vl_utils import process_vision_info
+from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+
+def OCRmodel():
+
+    # default: Load the model on the available device(s)
+    model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True, torch_dtype=torch.float32)
+    # default processer
+    processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True)
+    
+    # Move the model to the correct device (GPU if available, otherwise CPU)
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model.to(device)
+
+    return model, processor
+
+model, processor = OCRmodel()
+
+# Function to read the image and process it for OCR
+def ocr(image_data):
+    """
+    Process the uploaded image and extract text using the OCR model.
+
+    Args:
+    image_data: Image data in bytes.
+
+    Returns:
+    Extracted text as a string.
+    """
+    text_query = "Extract all the text in Sanskrit and English from the image."
+    # Prepare messages for the model with the image
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "image": image_data}, 
+                {"type": "text", "text": text_query}],
+        }
+    ]
+
+    # Prepare text and image input for the model
+    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    image_inputs, _ = process_vision_info(messages)
+    
+    # Process inputs
+    inputs = processor(
+        text=[text], 
+        images=image_inputs, 
+        padding=True, 
+        return_tensors="pt"
+    )
+    inputs = inputs.to("cuda" if torch.cuda.is_available() else "cpu")  # Move to GPU if available
+
+    # Generate the output from the model
+    with torch.no_grad():
+        # Inference: Generation of the output
+        generated_ids = model.generate(**inputs, max_new_tokens=2000, no_repeat_ngram_size=3, temperature=0.7)
+        generated_ids_trimmed = [
+            out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+        ]
+        output_text = processor.batch_decode(
+            generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )
+    return " ".join(output_text).strip()
+
+# Function to highlight search terms in the text
+def highlight_keywords(text, keywords):
+    pattern = "|".join(re.escape(keyword) for keyword in keywords)
+    highlighted_text = re.sub(f"({pattern})", rf'<mark style="background-color:{"red"};">\1</mark>', text, flags=re.IGNORECASE)
+    return highlighted_text
+
+# Gradio interface function
+def process_image(image, search_query):
+    
+    extracted_text = ocr(image)
+    
+    if search_query:
+        # Highlight matching keywords
+        keywords = search_query.split()  # Split input into individual keywords
+        highlighted_text = highlight_keywords(extracted_text, keywords)
+    else:
+        highlighted_text = extracted_text
+    
+    return highlighted_text
+
+# Gradio Interface
+application = gr.Interface(
+    fn=process_image,  # Function to process the image and search query
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),  # Image input
+        gr.Textbox(label="Enter search keywords")  # Textbox for search query
+    ],
+    outputs=gr.HTML(label="Extracted and Highlighted Text")  # Output area
+)
+
+# Launch the Gradio app
+application.launch(share=True)

requirements.txt

@@ -0,0 +1,6 @@
+git+https://github.com/huggingface/transformers
+torch==2.4.1+cu121
+torchvision==0.19.1+cu121
+gradio==4.44.0
+qwen-vl-utils==0.0.8
+pillow==10.4.0