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import streamlit as st
import torch
from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
from PIL import Image
# Load the model and processor
model_id = "brucewayne0459/paligemma_derm"
processor = AutoProcessor.from_pretrained(model_id)
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id, device_map={"": 0})
model.eval()
# Set device
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
# Streamlit app
st.title("Skin Condition Identifier")
st.write("Upload an image and provide a text prompt to identify the skin condition.")
# File uploader for image
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
# Text input for prompt
input_text = st.text_input("Enter your prompt:", "Identify the skin condition?")
# Process and display the result when the button is clicked
if uploaded_file is not None and st.button("Analyze"):
try:
# Open the uploaded image
input_image = Image.open(uploaded_file).convert("RGB")
st.image(input_image, caption="Uploaded Image", use_column_width=True)
# Prepare inputs
inputs = processor(
text=input_text,
images=input_image,
return_tensors="pt",
padding="longest"
).to(device)
# Generate output
max_new_tokens = 50
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=max_new_tokens)
# Decode output
decoded_output = processor.decode(outputs[0], skip_special_tokens=True)
# Display result
st.success("Analysis Complete!")
st.write("**Model Output:**", decoded_output)
except Exception as e:
st.error(f"Error: {str(e)}")
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