Update app.py

app.py

@@ -4,7 +4,9 @@ from tensorflow.keras.preprocessing import image
 import numpy as np
 
 # Load the trained model
-model = tf.keras.models.load_model('Model1_kera.h5')
+model = tf.keras.models.load_model('Model1_kera.h5', compile=False)
+model.compile(Adamax(learning_rate= 0.001), loss= 'categorical_crossentropy', metrics= ['accuracy'])
+# model = tf.keras.models.load_model('Model1_kera.h5')
 
 # Define the class names
 classes = ['Colon Adenocarcinoma', 'Colon Benign Tissue', 'Lung Adenocarcinoma', 'Lung Benign Tissue', 'Lung Squamous Cell Carcinoma']
@@ -14,11 +16,15 @@ def predict(img):
     img = img.resize((224, 224))
     img_array = tf.keras.preprocessing.image.img_to_array(img)
     img_array = tf.expand_dims(img_array, 0)
-
+    
     predictions = model.predict(img_array)
-    predicted_class = classes[np.argmax(predictions[0])]
+    class_labels = classes
+    # predictions = model.predict(img_array)
+    # predicted_class = classes[np.argmax(predictions[0])]
+    score = tf.nn.softmax(predictions[0])
+    print(f"{class_labels[tf.argmax(score)]}") 
     
-    return predicted_class
+    return f"{class_labels[tf.argmax(score)]}
 
 # Create a Gradio interface
 iface = gr.Interface(