Update app.py

app.py

@@ -1,31 +1,28 @@
-import gradio as gr
+import requests
+import tempfile
 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')
+# URL to the model file in your Hugging Face repository
+url = 'https://huggingface.co/datasets/yolac/BacterialMorphologyClassification'
+# Download the model file
+response = requests.get(url, stream=True)
 
-# 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
+# Check if the download was successful
+if response.status_code == 200:
+    # Create a temporary file to save the model
+    with tempfile.NamedTemporaryFile(suffix=".h5", delete=False) as temp_file:
+        for chunk in response.iter_content(chunk_size=8192):
+            if chunk:
+                temp_file.write(chunk)
+        temp_file_path = temp_file.name  # Get the path to the temporary file
 
-# 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."
-)
+    # Load the pre-trained model
+    try:
+        model = tf.keras.models.load_model(temp_file_path)
+        print("Model loaded successfully.")
+    except OSError as e:
+        print(f"Error loading the model: {e}")
+else:
+    print("Failed to download the model. Status code:", response.status_code)
 
-# Launch the app
-if __name__ == "__main__":
-    iface.launch()
+# Now you can use the `model` object for predictions or further processing.