app.py

import streamlit as st
from tensorflow import keras
import numpy as np
from huggingface_hub import HfFileSystem
from PIL import Image

# Authenticate and download the custom model from Hugging Face Spaces
fs = HfFileSystem()
model_path = 'dhhd255/main_model/best_model.h5'
with fs.open(model_path, 'rb') as f:
    model_content = f.read()

# Save the model file to disk
with open('best_model.h5', 'wb') as f:
    f.write(model_content)

# Load your custom model
model = keras.models.load_model('best_model.h5')

# Define a function that takes an image as input and uses the model for inference
def image_classifier(image):
    # Preprocess the input image
    image = Image.fromarray(image)
    image = image.convert('L')
    image = image.resize((128, 128))
    image = np.array(image)
    image = image / 255.0
    image = np.expand_dims(image, axis=0)
    image = np.expand_dims(image, axis=-1)
    
    # Use your custom model for inference
    predictions = model.predict(image)
    
    # Get the index of the highest predicted probability
    predicted_index = np.argmax(predictions[0])
    
    # Map the index to a class label
    labels = ['Healthy', 'Parkinson']
    predicted_label = labels[predicted_index]
    
    # Return the result
    return predictions[0], predicted_label

# Create a Streamlit app with an image upload input
uploaded_file = st.file_uploader('Upload an image')
if uploaded_file is not None:
    # Convert the UploadedFile object to a NumPy array
    image = Image.open(uploaded_file)
    image = np.array(image)
    
    # Use the image for inference
    predictions, predicted_label = image_classifier(image)
    
    # Display the result
    st.write(f'Predictions: {predictions}')
    st.write(f'Predicted label: {predicted_label}')