Create app.py

app.py

@@ -0,0 +1,43 @@
+import gradio as gr
+import torch
+from torchvision import transforms
+from torchvision.models import resnet18, ResNet18_Weights
+from torch import nn
+from PIL import Image # pip install pillow
+
+labels = ['Fractured','Non-fractured']
+
+# Same data transformation that was used for inputs (except data augmentation)
+data_transform = transforms.Compose([
+    transforms.Resize(size=(256, 256)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                        std=[0.229, 0.224, 0.225])
+])
+
+# https://pytorch.org/tutorials/beginner/saving_loading_models.html
+# Loading Model for Inference with state_dict (recommended)
+model = resnet18(weights=ResNet18_Weights.DEFAULT)
+model.fc = nn.Linear(in_features=512, out_features=len(labels))
+model.load_state_dict(torch.load("model.pth",map_location=torch.device('cpu')))
+model.eval()
+
+def predict(img):
+    X = data_transform(img).unsqueeze(0) # returns tensor
+    with torch.no_grad():
+            predictions = model(X).flatten()
+            predictions = torch.nn.functional.softmax(predictions)
+            confidences = {labels[i]: float(predictions[i]) for i in range(len(labels))}
+    return confidences
+
+title = "Corn Leaf Diseases"
+description = "A corn leaf disease classifier trained on the Kaggle dataset using Resnet18"
+
+demo=gr.Interface(fn=predict,
+             inputs=gr.Image(type="pil"),
+             outputs=gr.Label(num_top_classes=len(labels)),
+             title=title,
+             description=description,     
+             examples=["2.jpg", "Corn_Common_Rust.jpg"])
+
+demo.launch('share=True')