model_update

app.py

@@ -24,9 +24,13 @@ import numpy as np
 import tensorflow as tf
 from transformers import pipeline
 
+
 # load the binary classification model
 model_binary = tf.keras.models.load_model("CNN_binary")
 
+# define the labels for the binary classification model
+labels_binary = {0: 'healthy', 1: 'Patients'}
+
 # load the multi-label classification model
 model_multi = tf.keras.models.load_model("CNN_multiclass")
 
@@ -34,37 +38,40 @@ model_multi = tf.keras.models.load_model("CNN_multiclass")
 labels_multi = {0: 'healthy', 1: 'mild', 2: 'moderate'}
 
 def classify_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}
+    inp = inp.reshape((-1, 224, 224, 3))
+    inp = tf.keras.applications.densenet.preprocess_input(inp)
+    prediction = model_binary.predict(inp)
+    confidence = float(prediction[0])
+    return {labels_binary[prediction.argmax()]: confidence}
 
 def classify_multi(inp):
-  inp = inp.reshape((-1, 224, 224, 3))
-  inp = tf.keras.applications.densenet.preprocess_input(inp)
-  prediction = model_multi.predict(inp)
-  confidences = {labels_multi[i]: float(prediction[0][i]) for i in range(len(labels_multi))}
-  return confidences
+    inp = inp.reshape((-1, 224, 224, 3))
+    inp = tf.keras.applications.densenet.preprocess_input(inp)
+    prediction = model_multi.predict(inp)
+    confidences = {labels_multi[i]: float(prediction[0][i]) for i in range(len(labels_multi))}
+    return confidences
 
 binary_interface = gr.Interface(fn=classify_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']]
-             )
+                                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']]
+                               )
 
 multi_interface = gr.Interface(fn=classify_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']]
-             )
-
-demo = gr.TabbedInterface([binary_interface, multi_interface], ["Binary", "Multi-class"])
-
+                               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']]
+                              )
+
+demo = gr.Interface([binary_interface, multi_interface], 
+                     "tab",
+                     title="Binary and Multi-class Image Classification",
+                     description="Classify an image as healthy, mild or moderate.")
 demo.launch()
 
+             
+