Create app.py

app.py

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+import streamlit as st
+import torch
+from PIL import Image
+from byaldi import RAGMultiModalModel
+import tempfile
+
+# Function to upload image, run inference, and display output
+def upload_image_and_infer():
+    # Step 1: Allow user to upload an image file
+    uploaded_file = st.file_uploader("Upload an image file", type=["jpg", "png", "jpeg"])
+    
+    if uploaded_file is not None:
+        # Step 2: Save uploaded image to temporary file
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
+            temp_file.write(uploaded_file.read())
+            temp_path = temp_file.name
+
+        # Step 3: Display the uploaded image
+        image = Image.open(temp_path)
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+
+        # Step 4: Load the RAGMultiModalModel and processor
+        RAG = RAGMultiModalModel.from_pretrained("vidore/colpali")
+        model = Qwen2VLForConditionalGeneration.from_pretrained(
+            "Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int8", torch_dtype="auto", device_map="auto"
+        )
+        processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int8")
+        
+        # Assuming `results` contains the page number information
+        text_query = "extract the details?"
+        RAG.index(
+            input_path=temp_path,  # Using the uploaded image's temporary path
+            index_name="image_index",
+            store_collection_with_index=False,
+            overwrite=True
+        )
+        results = RAG.search(text_query, k=1)
+        
+        # Step 5: Prepare messages for inference
+        image_index = results[0]["page_num"] - 1  # Get page number from the search result
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image",
+                        "image": image,  # Use the uploaded image
+                    },
+                    {"type": "text", "text": text_query},
+                ],
+            }
+        ]
+
+        # Step 6: Prepare input for the model
+        text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        image_inputs, video_inputs = process_vision_info(messages)  # Assuming process_vision_info is defined
+        
+        # Tokenizing and preparing inputs
+        inputs = processor(
+            text=[text],
+            images=image_inputs,
+            videos=video_inputs,
+            padding=True,
+            return_tensors="pt",
+        )
+        inputs = inputs.to("cuda")
+        
+        # Step 7: Inference and generate output
+        generated_ids = model.generate(**inputs, max_new_tokens=128)
+        generated_ids_trimmed = [
+            out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+        ]
+        
+        # Decode the generated output
+        output_text = processor.batch_decode(
+            generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )
+
+        # Step 8: Display the output in Streamlit
+        st.write("Generated Output:", output_text)
+    else:
+        st.write("Please upload an image.")
+
+# Helper function to process images (replace with actual implementation if needed)
+def process_vision_info(messages):
+    image_inputs = [msg['content'][0]['image'] for msg in messages if 'image' in msg['content'][0]]
+    video_inputs = []  # Assuming no video inputs for now
+    return image_inputs, video_inputs
+
+# Run the function inside the Streamlit app
+upload_image_and_infer()