v3.0.1-Test Image Classifier with Pytorch

app.py

@@ -1,20 +1,41 @@
 import gradio as gr
 import numpy as np
+import torch
+import requests
+from PIL import Image
+from torchvision import transforms
 
-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
+model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Download human-readable labels for ImageNet.
+response = requests.get("https://git.io/JJkYN")
+labels = response.text.split("\n")
 
-demo = gr.Interface(fn=sepia, inputs="image", outputs="image")
+# 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
+
+# def greet(name):
+#     return "Hello " + name + "!!"
+
+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
+
+# demo = gr.Interface(fn=sepia, inputs="image", outputs="image")
+demo = gr.Interface(fn=predict,
+                    inputs=gr.Image(type="pil"),
+                    outputs=gr.Label(num_top_classes=3),
+                    examples=["lion.jpg", "cheetah.jpg"])
 demo.launch()
 
 # iface = gr.Interface(fn=greet, inputs="text", outputs="text")

requirements.txt

@@ -1,2 +1,6 @@
 gradio
-numpy
+numpy
+torch
+requests
+PIL 
+torchvision