Create app.py

app.py

@@ -0,0 +1,62 @@
+import os
+import copy
+import torch
+import gradio
+import gradio as gr
+from PIL import Image
+device = torch.device('cuda' if torch.cuda.is_available()  else 'cpu')
+
+os.system("wget https://www.dropbox.com/s/grcragozd4x79zc/model_ok.pth")
+
+model = torch.load("./model_ok.pth", map_location=device)
+
+# img  = Image.open(path).convert('RGB')
+from torchvision import transforms
+
+transforms2 =  transforms.Compose([
+        transforms.Resize(256),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+# img = transforms(img)
+# img = img.unsqueeze(0)
+model.eval()
+
+labels = ['aunts','bees']
+
+# with torch.no_grad():
+#   # preds = 
+#   preds = model(img)
+#   score, indices = torch.max(preds, 1)
+
+def recognize_digit(image):
+    image = transforms2(image)
+    image = image.unsqueeze(0)
+    # image = image.unsqueeze(0)
+    # image = image.reshape(1, -1)
+    # with torch.no_grad():
+    # preds = 
+    # img = image.reshape((-1, 3, 256, 256))
+    preds = model(image).flatten()
+      # prediction = model.predict(image).tolist()[0]
+    # score, indices = torch.max(preds, 1)
+    # return {str(indices.item())}
+    return {labels[i]: float(preds[i]) for i in range(2)}
+
+
+im = gradio.inputs.Image(
+    shape=(256, 256), image_mode="RGB", type="pil")
+
+iface = gr.Interface(
+    recognize_digit,
+    im,
+    gradio.outputs.Label(num_top_classes=3),
+    live=True,
+    interpretation="default",
+    examples=[["images/cheetah1.jpg"], ["images/lion.jpg"]],
+    capture_session=True,
+)
+
+iface.test_launch()
+iface.launch()