Create main.py

main.py

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+import gradio as gr
+from tensorflow import keras
+import numpy as np
+from huggingface_hub import HfApi
+import h5py
+from io import BytesIO
+
+# Authenticate and read 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)
+model_file = h5py.File(BytesIO(r.content), 'r')
+
+# Load your custom model
+model = keras.models.load_model(model_file)
+
+def image_classifier(inp):
+    # Preprocess the input image
+    inp = np.array(inp)
+    inp = inp / 255.0
+    inp = np.expand_dims(inp, axis=0)
+    
+    # Use your custom model for inference
+    predictions = model.predict(inp)
+    
+    # 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
+
+demo = gr.Interface(fn=image_classifier, inputs='image', outputs='label')
+demo.launch()