update_two

app.py

@@ -20,26 +20,37 @@ from transformers import pipeline
 #binary_labels = {0: 'healthy', 1: 'patient'}
 
 # load the binary classification model
-model_binary = tf.keras.models.load_model("densenet")
+#model_binary = tf.keras.models.load_model("densenet")
 
 # load the multi-label classification model
-model_multi = tf.keras.models.load_model("densenet")
+#model_multi = tf.keras.models.load_model("densenet")
 
 # define the labels for the multi-label classification model
-labels_multi = {0: 'healthy', 1: 'mild', 2: 'moderate'}
+#labels_multi = {0: 'healthy', 1: 'mild', 2: 'moderate'}
 
 
 #model = tf.keras.models.load_model('/content/drive/MyDrive/project_image_2023_NO/saved_models/saved_model/densenet')
 #labels = ['Healthy', 'Patient']
 #labels = {0: 'healthy', 1: 'patient'}
 
+
+# load the binary classification model
+model_binary = tf.keras.models.load_model("densenet")
+
+# load the multi-label classification model
+model_multi = tf.keras.models.load_model("densenet")
+
+# define the labels for the multi-label classification model
+labels_multi = {0: 'healthy', 1: 'mild', 2: 'moderate'}
+
+
 def classify_image_binary(inp):
   inp = inp.reshape((-1, 224, 224, 3))
   inp = tf.keras.applications.densenet.preprocess_input(inp)
   prediction = model_binary.predict(inp)
   confidence = float(prediction[0])
   label = {0: 'healthy', 1: 'patient'}
-  return {label: confidence}
+  return {label[1]: confidence}
 
 
 def classify_image_multi(inp):
@@ -49,21 +60,22 @@ def classify_image_multi(inp):
   confidences = {labels_multi[i]: float(prediction[0][i]) for i in range(3)}
   return confidences
     
-iface_binary = gr.Interface(fn=classify_image_binary, 
-             inputs=gr.Image(shape=(224, 224)),
-             outputs=gr.Label(num_top_classes=2),
+binary_interface = gr.Interface(fn=classify_image_binary, 
+             inputs=gr.inputs.Image(shape=(224, 224)),
+             outputs=gr.outputs.Label(num_top_classes=2),
              title="Binary Image Classification",
              description="Classify an image as healthy or patient.",
              examples=[['300104.png']]
              )
 
-iface_multi = gr.Interface(fn=classify_image_multi, 
-             inputs=gr.Image(shape=(224, 224)),
-             outputs=gr.Label(num_top_classes=3),
-             title="Multi-Label Image Classification",
-             description="Classify an image as healthy, mild, or moderate.",
+multi_interface = gr.Interface(fn=classify_image_multi, 
+             inputs=gr.inputs.Image(shape=(224, 224)),
+             outputs=gr.outputs.Label(num_top_classes=3),
+             title="Multi-class Image Classification",
+             description="Classify an image as healthy, mild or moderate.",
              examples=[['300104.png']]
              )
 
-iface_binary.launch()
-iface_multi.launch()
+binary_interface.launch()
+multi_interface.launch()
+