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import streamlit as st
from tensorflow import keras
import numpy as np
from huggingface_hub import HfApi
# Authenticate and download the custom model from Hugging Face Spaces
hf_api = HfApi()
model_url = hf_api.presigned_url('dhhd255', 'idk_test', filename='best_model.h5', token='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')
r = requests.get(model_url)
with open('best_model.h5', 'wb') as f:
f.write(r.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 = np.array(image)
image = image / 255.0
image = np.expand_dims(image, axis=0)
# 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
image = st.file_uploader('Upload an image')
if image is not None:
result = image_classifier(image)
st.write(result)
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