Create app.py

app.py

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+import os
+import copy
+import torch
+import gradio
+import gradio as gr
+from PIL import Image
+import torch.nn as nn
+from torchvision import transforms, models
+device = torch.device('cuda' if torch.cuda.is_available()  else 'cpu')
+
+os.system("wget https://www.dropbox.com/s/3us120bz5lhoh0t/model_best.pt?dl=0")
+
+model = models.resnet50(pretrained=True)
+num_ftrs = model.fc.in_features
+# Here the size of each output sample is set to 2.
+# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).
+model.fc = nn.Linear(num_ftrs, 7)
+
+model.load_state_dict(torch.load("./model_best.pt?dl=0", 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 = ["Bacterialblight",
+"Blast",
+"Brownspot",
+"Healthy",
+"Hispa",
+"LeafBlast",
+"Tungro"]
+# 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(7)}
+
+
+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()