two_interface

app.py

@@ -33,58 +33,37 @@ from transformers import pipeline
 #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 binary classification model
-labels_binary = {0: 'healthy', 1: 'patient'}
-
-# define the labels for the multi-label classification model
-labels_multi = {0: 'healthy', 1: 'mild', 2: 'moderate'}
-
-
-# define the function for the binary classification model
-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 = labels_binary[int(np.round(confidence))]
-    return {label: confidence}
-
-
-# define the function for the multi-label classification model
-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(3)}
-    return confidences
-
-
-# create the Gradio interface for the binary classification model
-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']]
-                                )
-
-# create the Gradio interface for the multi-label classification model
-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']]
-                               )
-
-# create the Gradio app with both interfaces
-app = gr.Interface([binary_interface, multi_interface], title="Image Classification App")
-app.launch()
+# Load the model
+model = tf.keras.models.load_model("densenet")
+
+# Define the labels
+labels = {0: 'healthy', 1: 'patient'}
+
+def classify_image(inp):
+  inp = inp.reshape((-1, 224, 224, 3))
+  inp = tf.keras.applications.densenet.preprocess_input(inp)
+  prediction = model.predict(inp)
+  confidences = {labels[i]: float(prediction[0][i]) for i in range(2)}
+  return confidences
+
+# Create the binary interface
+binary_interface = gr.Interface(fn=classify_image, 
+             inputs=gr.Image(shape=(224, 224)),
+             outputs=gr.Label(num_top_classes=2),
+             title="Binary Image Classification",
+             description="Classify an image as healthy or patient.",
+             examples=[['300104.png']]
+             )
+
+# Create the multi-class interface
+multi_interface = gr.Interface(fn=classify_image, 
+             inputs=gr.Image(shape=(224, 224)),
+             outputs=gr.Label(num_top_classes=3),
+             title="Multi-class Image Classification",
+             description="Classify an image as healthy, mild or moderate.",
+             examples=[['300104.png']]
+             )
+
+# Launch the app
+gr.Interface([binary_interface, multi_interface], "grid", title="My Image Classification App").launch()