initial files

README.md

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-# image-classifier-gradio-demo
-Serve Image classification model with Gradio and Hugging Face Spaces.
+---
+title: Image Recognition Demo
+emoji: 🚀
+colorFrom: indigo
+colorTo: pink
+sdk: gradio
+sdk_version: 2.9.4
+app_file: app.py
+pinned: false
+license: afl-3.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

app.py

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+import torch
+import requests
+import gradio as gr
+
+from torchvision import transforms
+
+"""
+Built following https://www.gradio.app/image_classification_in_pytorch/.
+"""
+
+# Load model
+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 = 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.inputs.Image(type="pil"),
+             outputs=gr.outputs.Label(num_top_classes=3),
+             examples=["example1.jpg", "example2.jpg"],
+             theme="default",
+             css=".footer{display:none !important}").launch()

requirements.txt

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+torch
+torchvision