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demo/demo1.py

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+# pip install gradio
+# pip install python-opencv
+
+
+import gradio as gr
+import cv2
+
+def to_black(image):
+    output = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    return output
+
+# GUI      http://127.0.0.1:7860/
+interface = gr.Interface(fn=to_black, inputs="image", outputs="image",  examples=[["test.png"]])
+interface.launch()
+

demo/demo2.py

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+import gradio as gr
+import torch
+from torchvision import transforms
+import requests
+from PIL import Image
+​
+model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
+​
+# 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)
+  return {labels[i]: float(prediction[i]) for i in range(1000)}
+​
+inputs = gr.inputs.Image()
+outputs = gr.outputs.Label(num_top_classes=3)
+gr.Interface(fn=predict, inputs=inputs, outputs=outputs).launch()

requirements.txt

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+gradio