new app

app.py

@@ -1,23 +1,17 @@
-import requests
+import gradio as gr
 import tensorflow as tf
 
-inception_net = tf.keras.applications.MobileNetV2()
-
-# Download human-readable labels for ImageNet.
-response = requests.get("https://git.io/JJkYN")
-labels = response.text.split("\n")
+new_model = tf.keras.models.load_model('my_model.h5')
+# preprocess_input = tf.keras.applications.resnet50.preprocess_input
 
 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
+    inp = inp.reshape((-1, 224, 224, 3))
+    prediction = new_model.predict(inp).flatten()
+    return prediction
 
 
-import gradio as gr
-
 gr.Interface(fn=classify_image, 
              inputs=gr.inputs.Image(shape=(224, 224)),
-             outputs=gr.outputs.Label(num_top_classes=3),
-             examples=["banana.jpg", "car.jpg"]).launch()
+             outputs="label",
+             examples=[]).launch()
+