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import streamlit as st
import torch
from PIL import Image
from transformers import AutoProcessor, AutoModelForImageTextToText
# 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.")
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