Update app.py

app.py

@@ -42,24 +42,39 @@ model_efn = tf.keras.models.load_model("efficientNet_binary")
 # define the labels for the binary classification model
 labels_efn = {0: 'Healthy', 1: 'Patients'}
 
+#def classify_cnn(inp):
+    #inp = inp.reshape((-1, 224, 224, 3))
+    #inp = tf.keras.applications.densenet.preprocess_input(inp)
+    #prediction = model_cnn.predict(inp)
+    #confidences = {labels_cnn[i]: float(prediction[0][i]) for i in range(2)}
+    #return confidences
+
+#def classify_efn(inp):
+    #inp = inp.reshape((-1, 224, 224, 3))
+    #inp = tf.keras.applications.efficientnet.preprocess_input(inp)
+    #prediction = model_efn.predict(inp)
+    #confidences = {labels_efn[i]: float(prediction[0][i]) for i in range(2)}
+    #return confidences
+
 def classify_cnn(inp):
     inp = inp.reshape((-1, 224, 224, 3))
     inp = tf.keras.applications.densenet.preprocess_input(inp)
     prediction = model_cnn.predict(inp)
-    confidences = {labels_cnn[i]: float(prediction[1][i]) for i in range(2)}
-    return confidences
+    class_index = np.argmax(prediction, axis=-1)[0]
+    return labels_cnn[class_index]
 
 def classify_efn(inp):
     inp = inp.reshape((-1, 224, 224, 3))
     inp = tf.keras.applications.efficientnet.preprocess_input(inp)
     prediction = model_efn.predict(inp)
-    confidences = {labels_efn[i]: float(prediction[1][i]) for i in range(2)}
-    return confidences
+    class_index = np.argmax(prediction, axis=-1)[0]
+    return labels_efn[class_index]
 
 
 binary_interface_cnn = gr.Interface(fn=classify_cnn, 
                                 inputs=gr.Image(shape=(224, 224)),
-                                outputs=gr.Label(num_top_classes=2),
+                                #outputs=gr.Label(num_top_classes=2),
+                                outputs=gr.outputs.Textbox(),
                                 title="Binary Image Classification",
                                 description="Classify an image as healthy or patient using custom CNN.",
                                 examples=[['3310277.png'],['371129.png']]
@@ -68,13 +83,17 @@ binary_interface_cnn = gr.Interface(fn=classify_cnn,
 
 binary_interface_efn = gr.Interface(fn=classify_efn, 
                                inputs=gr.Image(shape=(224, 224)),
-                               outputs=gr.Label(num_top_classes=2),
+                               #outputs=gr.Label(num_top_classes=2),
+                               outputs=gr.outputs.Textbox(),
                                title="Binary Image Classification",
                                description="Classify an image as healthy or patient using EfficientNet.",
                                examples=[['3310277.png'],['371129.png']]
                               )
 
 
+
+
+
 demo = gr.TabbedInterface([binary_interface_cnn, binary_interface_efn], ["Custom CNN", "CNNs"])
 
 demo.launch()