Update app.py

app.py

@@ -3,4 +3,48 @@ import numpy as np
 from tensorflow.keras.models import load_model
 from PIL import Image
 
-model = load_h5_model('cell_classifier_model.h5')
+def load_h5_model(model_path):
+    loaded_model = load_model(model_path)
+    return(loaded_model)
+
+def prepro_img(img):
+    # resize the image to 130x130
+    img = img.resize((130,130))
+    # converto it to array with shape (1,130,130,3)
+    img_array = np.array(img)
+    img_array = np.array([img_array])
+    # return result
+    return(img_array)
+
+def make_prediction(img):
+    # preprocess image
+    img = prepro_img(img)
+    # make prediction
+    prediction = model.predict(img)
+    prediction = int(prediction[0][0])
+    # return prediction label
+    if prediction == 1:
+        return('Uninfected cell')
+    else:
+        return('Parasitized cell')    
+
+model = load_h5_model('cell_classifier_model.h5')
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            cell_img = gr.Image(label="Cell Image",
+                                type='pil')
+            examples = gr.Examples(['para_1.png', 'para_2.png', 'para_3.png'],
+                                   inputs=cell_img,
+                                   label='Parasitized Cells')
+            examples = gr.Examples(['uninf_1.png', 'uninf_2.png', 'uninf_3.png'],
+                                   inputs=cell_img,
+                                   label='Uninfected Cells')
+        with gr.Column():
+            cell_class = gr.Label(value='...')
+
+    predict_btn = gr.Button("Predict")
+    predict_btn.click(fn=make_prediction, inputs=cell_img, outputs=cell_class)
+
+demo.launch(debug=True)