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# app.py | |
import streamlit as st | |
from transformers import pipeline | |
from PIL import Image | |
import requests | |
# Load the Hugging Face pipeline for image classification | |
classifier = pipeline("image-classification", model="imfarzanansari/skintelligent-acne") | |
# Title of the Streamlit app | |
st.title("Skin Condition Classification - Acne Detection") | |
# Upload an image file | |
uploaded_file = st.file_uploader("Choose an image...", type="jpg") | |
if uploaded_file is not None: | |
# Display the uploaded image | |
image = Image.open(uploaded_file) | |
st.image(image, caption="Uploaded Image", use_column_width=True) | |
# Perform image classification using the Hugging Face pipeline | |
st.write("Classifying...") | |
predictions = classifier(image) | |
# Display the results | |
for pred in predictions: | |
st.write(f"Label: {pred['label']}, Score: {pred['score']:.4f}") | |