Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.utils import load_img, img_to_array
+
+# Load the pre-trained model
+model = tf.keras.models.load_model('https://huggingface.co/yolac/BacterialMorphologyClassification/resolve/main/model')
+
+# Define a function for making predictions
+def predict_bacterial_morphology(image):
+    # Preprocess the input image
+    img_array = img_to_array(image) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    # Make a prediction
+    prediction = model.predict(img_array, verbose=0)
+    class_labels = ['cocci', 'bacilli', 'spirilla']
+    predicted_label = class_labels[np.argmax(prediction)]
+    return predicted_label
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=predict_bacterial_morphology,
+    inputs=gr.inputs.Image(shape=(224, 224)),
+    outputs=gr.outputs.Textbox(label="Predicted Class"),
+    title="Bacterial Morphology Classification",
+    description="Upload an image of a bacterium to classify it into one of the following categories: cocci, bacilli, or spirilla."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()