Update app.py

app.py

@@ -1,27 +1,26 @@
+import torch
 import requests
-import tensorflow as tf
+from PIL import Image
+from torchvision import transforms
 
-import gradio as gr
+model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
 
-inception_net = tf.keras.applications.MobileNetV2()  # load the model
 
 # Download human-readable labels for ImageNet.
 response = requests.get("https://git.io/JJkYN")
 labels = response.text.split("\n")
 
+def predict(inp):
+  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)}
+  return confidences
+    
+import gradio as gr
 
-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()
-    return {labels[i]: float(prediction[i]) for i in range(1000)}
-
-
-image = gr.Image(shape=(224, 224))
-label = gr.Label(num_top_classes=3)
-
-demo = gr.Interface(
-    fn=classify_image, inputs=image, outputs=label, interpretation="default"
-)
 
-demo.launch()
+gr.Interface(fn=predict,
+             inputs=gr.Image(type="pil"),
+             outputs=gr.Label(num_top_classes=3),
+             examples=["lion.jpg", "cheetah.jpg"]).launch()