Updated app

app.py

@@ -1,15 +1,37 @@
 import numpy as np
 import gradio as gr
+import torch
+
+from transformers import pipeline
+
+model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
+
+# def sepia(input_img):
+#     sepia_filter = np.array([
+#         [0.393, 0.769, 0.189], 
+#         [0.349, 0.686, 0.168], 
+#         [0.272, 0.534, 0.131]
+#     ])
+#     sepia_img = input_img.dot(sepia_filter.T)
+#     sepia_img /= sepia_img.max()
+#     return sepia_img
+
+# demo = gr.Interface(sepia, gr.Image(), "image")
+
+# 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
+
+gr.Interface(fn=predict,
+             inputs=gr.Image(type="pil"),
+             outputs=gr.Label(num_top_classes=3),
+             examples=["lion.jpg", "cheetah.jpg"]).launch()
 
-def sepia(input_img):
-    sepia_filter = np.array([
-        [0.393, 0.769, 0.189], 
-        [0.349, 0.686, 0.168], 
-        [0.272, 0.534, 0.131]
-    ])
-    sepia_img = input_img.dot(sepia_filter.T)
-    sepia_img /= sepia_img.max()
-    return sepia_img
-
-demo = gr.Interface(sepia, gr.Image(), "image")
 demo.launch()