Update app.py

app.py

@@ -1,28 +1,22 @@
-import torch
-
-model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
-
 import requests
-from PIL import Image
-from torchvision import transforms
+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")
 
-def predict(inp):
-  inp = Image.fromarray(inp.astype('uint8'), 'RGB')
-  inp = transforms.ToTensor()(inp).unsqueeze(0)
-  with torch.no_grad():
-    prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
-    confidences = {labels[i]: float(prediction[i]) for i in range(1000)}    
+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
+  
 
 import gradio as gr
 
 gr.Interface(fn=predict, 
-             inputs="image",
+             inputs=gr.inputs.Image(shape=(224, 224)),
              outputs=gr.outputs.Label(num_top_classes=3),
-             examples=["lion.jpg", "cheetah.jpg"],
+             examples=["banana.jpg", "car.jpg"]).launch(),             
              theme="default",
              css=".footer{display:none !important}").launch()