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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)
# Process the predictions and return the result
result = {}
for i, prediction in enumerate(predictions[0]):
label = f'Label {i+1}'
result[label] = prediction
return result
# 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
result = image_classifier(image)
st.write(result)
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