Update app.py

app.py

@@ -1,56 +1,26 @@
-"""
-import tensorflow as tf
-
-inception_net = tf.keras.applications.MobileNetV2()
-
-import requests
-
-# Download human-readable labels for ImageNet.
-response = requests.get("https://git.io/JJkYN")
-labels = response.text.split("\n")
-
-def classify_image(inp):
-    inp = inp.reshape((-1, 224, 224, 3))
-    inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
-    prediction = inception_net.predict(inp).flatten()
-    confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
-    return confidences
-    
-gr.Interface(fn=classify_image, 
-             inputs=gr.Image(shape=(224, 224)),
-             outputs=gr.Label(num_top_classes=3),
-             #examples=["banana.jpg", "car.jpg"]
-            ).launch(share=True)
-"""
-
 import gradio as gr
 import tensorflow as tf
 from tensorflow import keras
-import requests
 
 
 # load pre-trained model
 model_path = "/Users/chaninderrishi/Desktop/ML/projects/waste-sorting/models/prod3"
 pre_trained_model = keras.models.load_model(model_path)
 
+# classification labels
 labels = ['compost', 'e-waste', 'recycle', 'trash']
 
+
 def classify_image(input):
     prediction = pre_trained_model.predict(input) 
     confidences = {labels[i]: float(prediction[i]) for i in range(4)}
     return confidences
 
-
+# create Gradio interface
 iface = gr.Interface(fn=classify_image, 
              inputs=gr.Image(shape=(224, 224)),
              outputs=gr.Label(num_top_classes=3),
              #examples=["banana.jpg", "car.jpg"]
              )
              
-iface.launch(share=True)
-"""
-def greet(name):
-    return "Hello " + name + "!!"
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
-"""
+iface.launch(share=True)