image classification

app.py

@@ -1,13 +1,30 @@
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+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
+
+# 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
 
-gr.Interface.load(
-        "spaces/eugenesiow/remove-bg", 
-        inputs=[gr.Image(label="Input Image", source="webcam")]
-    ).launch()
-iface.launch(share=True)
+gr.Interface(fn=predict, 
+             inputs=gr.Image(type="pil"),
+             outputs=gr.Label(num_top_classes=3),
+             examples=["lion.jpg", "cheetah.jpg"]).launch()
+
+