Vision Processor

app.py

@@ -1,4 +1,80 @@
 import streamlit as st
+import torch
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForImageTextToText
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# Set page configuration
+st.set_page_config(page_title="Llama 3.2 Vision Model", page_icon="???")
+
+# Title and description
+st.title("Llama 3.2 Vision Model Inference")
+st.write("Upload an image and provide a prompt to get model insights!")
+
+# Load model and processor (consider caching to improve performance)
+@st.cache_resource
+def load_model():
+    try:
+        processor = AutoProcessor.from_pretrained("meta-llama/Llama-3.2-90B-Vision-Instruct")
+        model = AutoModelForImageTextToText.from_pretrained("meta-llama/Llama-3.2-90B-Vision-Instruct")
+        return processor, model
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+        return None, None
+
+# Inference function
+def generate_response(image, prompt):
+    processor, model = load_model()
+    
+    if not processor or not model:
+        return "Model could not be loaded."
+    
+    try:
+        # Prepare inputs
+        inputs = processor(images=image, text=prompt, return_tensors="pt")
+        
+        # Generate response
+        with torch.no_grad():
+            outputs = model.generate(**inputs)
+        
+        # Decode the response
+        response = processor.decode(outputs[0], skip_special_tokens=True)
+        return response
+    
+    except Exception as e:
+        st.error(f"Error during inference: {e}")
+        return "An error occurred during image processing."
+
+# Sidebar for user inputs
+st.sidebar.header("Image and Prompt")
+
+# Image uploader
+uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+# Prompt input
+prompt = st.sidebar.text_input("Enter your prompt:", 
+                                placeholder="Describe what you want to know about the image")
+
+# Main content area
+if uploaded_file is not None:
+    # Display uploaded image
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    
+    # Generate button
+    if st.sidebar.button("Generate Response"):
+        if prompt:
+            # Show loading spinner
+            with st.spinner("Generating response..."):
+                response = generate_response(image, prompt)
+            
+            # Display response
+            st.subheader("Model Response")
+            st.write(response)
+        else:
+            st.warning("Please enter a prompt!")
+else:
+    st.info("Upload an image and enter a prompt to get started!")
+
+# Additional error handling and information
+st.sidebar.markdown("---")
+st.sidebar.info("Note: Model performance depends on image quality and prompt specificity.")