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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)
model.eval()
# Set device
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
# Add Hugging Face logo at the top
st.markdown(
"""
<style>
.huggingface-logo {
display: flex;
justify-content: center;
margin-bottom: 20px;
}
.huggingface-logo img {
width: 150px;
}
</style>
<div class="huggingface-logo">
<img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" alt="Hugging Face Logo">
</div>
""",
unsafe_allow_html=True,
)
# Streamlit app title and instructions
st.title("Skin Condition Identifier")
st.write("Upload an image and provide a custom prompt to identify the skin condition.")
# Column layout for input and display
col1, col2 = st.columns([3, 2])
with col1:
# File uploader for image
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
prompt = 'Identify the skin condition?'
with col2:
if uploaded_file:
# Open and resize the uploaded image
input_image = Image.open(uploaded_file).convert("RGB")
resized_image = input_image.resize((300, 300))
st.image(resized_image, caption="Uploaded Image (300x300)", use_container_width=True)
# Resize image for processing (512x512 pixels)
max_size = (512, 512)
processed_image = input_image.resize(max_size)
# Predict automatically when the image is uploaded or the prompt changes
with st.spinner("Processing..."):
try:
# Prepare inputs
inputs = processor(
text=prompt,
images=processed_image,
return_tensors="pt",
padding="longest"
).to(device)
# Generate output
default_max_tokens = 50 # Set a default value for max tokens
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=default_max_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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