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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()
demo.launch()
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