Add application file

app.py

@@ -0,0 +1,69 @@
+import streamlit as st
+from PIL import Image
+from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
+from qwen_vl_utils import process_vision_info
+
+def load_model_and_processor():
+    model = Qwen2VLForConditionalGeneration.from_pretrained(
+        "Qwen/Qwen2-VL-2B-Instruct", torch_dtype="auto", device_map="auto"
+    )
+    processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
+    return model, processor
+
+st.title('Image OCR and RAG')
+
+with st.sidebar:
+    st.header("Upload your image")
+    uploaded_file = st.file_uploader("Upload an image...", type=["jpg", "jpeg", "png"])
+    if uploaded_file is not None:
+        st.success("Image uploaded successfully!")
+
+model, processor = load_model_and_processor()
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    try:
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image",
+                        "image": image,
+                    },
+                    {"type": "text", "text": "Extract all the text present in the image and give the output in JSON format"},
+                ],
+            }
+        ]
+
+        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",
+        )
+        inputs = inputs.to("cpu")
+
+        # Generate output using the model
+        generated_ids = model.generate(**inputs, max_new_tokens=300)
+        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
+        )
+
+        # Display the extracted text in JSON format
+        st.subheader("Extracted Text in JSON Format:")
+        st.json(output_text[0])
+
+    except Exception as e:
+        st.error(f"An error occurred: {str(e)}")
+
+else:
+    st.write("Please upload an image from the sidebar")