two_interface_3

app.py

@@ -2,6 +2,17 @@
 
 # %%capture
 # #Use capture to not show the output of installing the libraries!
+
+#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'}
+
+
+#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'}
+
 import gradio as gr
 import requests
 import torch
@@ -13,57 +24,47 @@ import numpy as np
 import tensorflow as tf
 from transformers import pipeline
 
-# load the model from the Hugging Face Model Hub
-#model = pipeline('image-classification', model='image_classification/densenet')
-#model = tf.keras.models.load_model("densenet")
-#binary_model = tf.keras.models.load_model("densenet")
-#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 model
-model = tf.keras.models.load_model("densenet")
-
-# Define the labels
-labels = {0: 'healthy', 1: 'patient'}
+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}
 
-def classify_image(inp):
+def classify_multi(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)}
+  prediction = model_multi.predict(inp)
+  confidences = {labels_multi[i]: float(prediction[0][i]) for i in range(len(labels_multi))}
   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),
+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 multi-class interface
-multi_interface = gr.Interface(fn=classify_image, 
-             inputs=gr.Image(shape=(224, 224)),
-             outputs=gr.Label(num_top_classes=3),
+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']]
              )
 
-# Launch the app
-gr.Interface([binary_interface, multi_interface], "grid", title="My Image Classification App").launch()
+demo = gr.TabbedInterface([binary_interface, multi_interface], ["Binary", "Multi-class"])
+
+demo.launch()