Create app.py

app.py

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+import os
+import copy
+import torch
+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_best.pth?dl=0")
+
+model = torch.load("/content/model_best.pth?dl=0", map_location=device)
+
+path = "/content/Potato-leaf-blight.jpeg"
+
+# 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 = ['Tomato_Late_blight', 'Tomato_healthy', 'Grape_healthy', 'Orange_Haunglongbing(Citrus_greening)', 'Soybeanhealthy', 'Squash_Powdery_mildew', 'Potato_healthy', 'Corn(maize)Northern_Leaf_Blight', 'Tomato_Early_blight', 'Tomato_Septoria_leaf_spot', 'Corn(maize)Cercospora_leaf_spot Gray_leaf_spot', 'Strawberry_Leaf_scorch', 'Peach_healthy', 'Apple_Apple_scab', 'Tomato_Tomato_Yellow_Leaf_Curl_Virus', 'Tomato_Bacterial_spot', 'Apple_Black_rot', 'Blueberry_healthy', 'Cherry(including_sour)Powdery_mildew', 'Peach_Bacterial_spot', 'Apple_Cedar_apple_rust', 'Tomato_Target_Spot', 'Pepper,_bell_healthy', 'Grape_Leaf_blight(Isariopsis_Leaf_Spot)', 'PotatoLate_blight', 'Tomato_Tomato_mosaic_virus', 'Strawberry_healthy', 'Apple_healthy', 'Grape_Black_rot', 'Potato_Early_blight', 'Cherry(including_sour)healthy', 'Corn(maize)Common_rust', 'GrapeEsca(Black_Measles)', 'Raspberryhealthy', 'Tomato_Leaf_Mold', 'Tomato_Spider_mites Two-spotted_spider_mite', 'Pepper,_bell_Bacterial_spot', 'Corn(maize)__healthy']
+
+# 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(38)}
+
+
+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()