Upload 14 files

cnn_model.py

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+import torch.nn as nn
+
+class custom_model(nn.Module):
+    def __init__(self, input_param: int, output_param: int):
+        super().__init__()      
+        self.layer_block1 = nn.Sequential(
+            nn.Conv2d(in_channels=input_param, out_channels=32, kernel_size=3),
+            nn.ReLU(),
+            nn.BatchNorm2d(num_features=32),
+            
+            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3),
+            nn.ReLU(),
+            nn.BatchNorm2d(num_features=64),
+            nn.MaxPool2d(kernel_size=2)    
+        )
+        
+        self.layer_block2 = nn.Sequential(
+            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3),
+            nn.ReLU(),
+            nn.BatchNorm2d(num_features=128),
+            
+            nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3),
+            nn.ReLU(),
+            nn.BatchNorm2d(num_features=128),
+            nn.MaxPool2d(kernel_size=2)    
+        )
+        
+        self.layer_block3 = nn.Sequential(
+            nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3),
+            nn.ReLU(),
+            nn.BatchNorm2d(num_features=256),
+            
+            nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3),
+            nn.ReLU(),
+            nn.BatchNorm2d(num_features=256),
+            nn.MaxPool2d(kernel_size=2)    
+        )
+        
+        self.layer_block4 = nn.Sequential(
+            nn.Flatten(),
+            nn.Linear(in_features=256*12*12, out_features=256),
+            nn.Dropout(0.5),
+            nn.Linear(in_features=256, out_features=output_param)
+        )
+        
+    def forward(self, x):
+        x = self.layer_block1(x)
+        x = self.layer_block2(x)
+        x = self.layer_block3(x)
+        x = self.layer_block4(x)
+        return x
+
+

main.py

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+import gradio as gr
+import os
+import torch
+from cnn_model import custom_model
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+from torchvision import transforms
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+class_name = ['Tomato___Bacterial_spot', 
+              'Tomato___Early_blight', 
+              'Tomato___Late_blight', 
+              'Tomato___Leaf_Mold', 
+              'Tomato___Septoria_leaf_spot', 
+              'Tomato___Spider_mites Two-spotted_spider_mite', 
+              'Tomato___Target_Spot', 
+              'Tomato___Tomato_Yellow_Leaf_Curl_Virus', 
+              'Tomato___Tomato_mosaic_virus', 
+              'Tomato___healthy']
+
+#Function for gradio
+def predict_gradio(img):
+    start_time = timer()
+    
+    image_transform = transforms.Compose([
+            transforms.Resize(128),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                                 std=[0.229, 0.224, 0.225]),
+        ])
+
+    
+    #Load model
+    model_path = r'Deployment\tomato_plants\model_2.pth'
+    loaded_model_2 = custom_model(input_param=3, output_param=10) 
+    loaded_model_2.load_state_dict(torch.load(f=model_path))
+    loaded_model_2 = loaded_model_2.to(device)
+    loaded_model_2.eval()
+    
+    with torch.inference_mode():
+        transformed_image = image_transform(img).unsqueeze(dim=0)
+
+        target_image_pred = loaded_model_2(transformed_image.to(device))
+    
+    pred_probs = torch.softmax(target_image_pred, dim=1)
+    pred_labels_and_probs = {class_name[i]: float(pred_probs[0][i]) for i in range(len(class_name))}
+    pred_time = round(timer() - start_time, 4)
+
+    return pred_labels_and_probs, pred_time
+
+a = 'Deployment\tomato_plants\examples\bacterial_spot.JPG'
+
+#Create title
+title = 'Tomato Plants Disease Detector'
+description = 'A custom CNN image classification model to detect 9 diseases on tomato plants'
+articile = 'Created at [Deploy the tomato plant diseases image classification by using Gradio](https://github.com/lakiet1609/Deploy-the-tomato-plant-diseases-image-classification-by-using-Gradio)'
+example_list = [['Deployment/tomato_plants/examples/' + example] for example in os.listdir(r'Deployment\tomato_plants\examples')]
+
+# Create the Gradio demo
+demo = gr.Interface(fn=predict_gradio,
+                    inputs=gr.Image(type='pil'),
+                    outputs=[gr.Label(num_top_classes=3, label='prediction'),
+                             gr.Number(label='Prediction time (second)')],
+                    examples=example_list,
+                    title=title,
+                    description=description,
+                    article=articile)
+
+demo.launch(debug=False,
+            share=True)

model_2.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8cc4a346644e5cbbd873289452f3c7456727a330c169f20cd372801728ee0da5
+size 42292827

requirements.txt

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+torch==1.12.0
+torchvision==0.13.0
+gradio==3.1.4