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import streamlit as st
import torch
from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
from PIL import Image
# Load model and processor
model_id = "brucewayne0459/paligemma_derm"
processor = AutoProcessor.from_pretrained(model_id)
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id)
model.eval()
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
# Logo (Hugging Face)
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,
)
# App Title
st.title("VisionDerm")
st.write("Upload an image or use your camera to identify the skin condition.")
# Layout
col1, col2 = st.columns([3, 2])
with col1:
# File uploader
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
# Camera input
camera_photo = st.camera_input("Take a photo")
prompt = 'Identify the skin condition?'
# Choose input image
input_image = None
if camera_photo:
input_image = Image.open(camera_photo)
elif uploaded_file:
try:
# Open and convert uploaded file to RGB
input_image = Image.open(uploaded_file).convert("RGB")
input_image = input_image.copy() # Detach from file pointer
except Exception as e:
st.error(f"Error loading image: {str(e)}")
input_image = None
# Display and process the image
with col2:
if input_image:
try:
# Display the uploaded or captured image
resized_image = input_image.resize((300, 300))
st.image(resized_image, caption="Selected Image (300x300)", use_container_width=True)
# Resize the image for processing (512x512 pixels)
max_size = (512, 512)
processed_image = input_image.resize(max_size)
with st.spinner("Processing..."):
# Prepare inputs for the model
inputs = processor(
text=prompt,
images=processed_image,
return_tensors="pt",
padding="longest"
).to(device)
# Generate output from the model
default_max_tokens = 50 # Default value for max tokens
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=default_max_tokens)
# Decode and clean the output
decoded_output = processor.decode(outputs[0], skip_special_tokens=True)
if prompt in decoded_output:
decoded_output = decoded_output.replace(prompt, "").strip()
decoded_output = decoded_output.title()
# Display the result
st.success("Analysis Complete!")
st.write("**Model Output:**", decoded_output)
except Exception as e:
st.error(f"Error: {str(e)}")
st.markdown("---")
# Team Information
st.info("""
### Team: Mahasigma Berprestasi
- **Muhammad Karov Ardava Barus** ; 103052300001
- **Akmal Yaasir Fauzaan** ; 103052300008
- **Farand Diy Dat Mahazalfaa** ; 103052300050
- **Hauzan Rafi Attallah**; 103052330011
""") |