deploying

app.py

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+import streamlit as st
+from PIL import Image
+import torch
+from transformers import Qwen2VLForConditionalGeneration, AutoProcessor, pipeline
+from colpali_engine.models import ColPali, ColPaliProcessor
+import os
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+token = os.getenv('HUGGINGFACE_TOKEN')
+model = pipeline("image-to-text", model="google/paligemma-3b-mix-448", use_auth_token=token)
+
+try:
+    model_colpali = ColPali.from_pretrained("vidore/colpali-v1.2", torch_dtype=torch.bfloat16).to(device)
+    processor_colpali = ColPaliProcessor.from_pretrained("google/paligemma-3b-mix-448")
+except Exception as e:
+    st.error(f"Error loading ColPali model or processor: {e}")
+    st.stop()
+
+try:
+    model_qwen = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct").to(device)
+    processor_qwen = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
+except Exception as e:
+    st.error(f"Error loading Qwen model or processor: {e}")
+    st.stop()
+
+st.title("OCR and Document Search Web Application")
+st.write("Upload an image containing text in both Hindi and English for OCR processing and keyword search.")
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    try:
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Uploaded Image.', use_column_width=True)
+        st.write("")
+
+        conversation = [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "Describe this image."}]}]
+        text_prompt = processor_qwen.apply_chat_template(conversation, add_generation_prompt=True)
+        inputs = processor_qwen(text=[text_prompt], images=[image], padding=True, return_tensors="pt").to(device)
+
+        with torch.no_grad():
+            output_ids = model_qwen.generate(**inputs, max_new_tokens=128)
+            generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
+            output_text = processor_qwen.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+
+        st.write("Extracted Text:")
+        st.write(output_text)
+
+        keyword = st.text_input("Enter a keyword to search in the extracted text:")
+        if keyword:
+            if keyword.lower() in output_text[0].lower():
+                st.write(f"Keyword '{keyword}' found in the text.")
+            else:
+                st.write(f"Keyword '{keyword}' not found in the text.")
+    except Exception as e:
+        st.error(f"An error occurred: {e}")
+
+if __name__ == "__main__":
+    st.write("Deploying the web application...")

requirements.txt

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+streamlit
+Pillow
+torch
+transformers
+colpali-engine