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@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/9.jpg filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,79 @@
+import gradio as gr
+import os
+import torch
+
+#from model import ResNet18_model
+#from transform import transforms_img
+
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+# Setup class names
+class_names = ["Anthracnose", "Chimaera", "Healthy Leaves"]
+
+### 2. Model and transforms preparation ###
+
+# Create model
+result_0 = ResNet18_model(num_classes = len(class_names))
+
+Transform = transforms_img
+
+# Load saved weights
+result_0.load_state_dict(torch.load(f="models/Palm_Leaves_ResNet18.pth",
+        map_location=torch.device("cpu"),  # load to CPU
+    )
+)
+
+### 3. Predict function ###
+
+# Create predict function
+from typing import Tuple, Dict
+
+def predict(img) -> Tuple[Dict, float]:
+    """Transforms and performs a prediction on img and returns prediction and time taken.
+    """
+    # Start the timer
+    start_time = timer()
+    
+    # Transform the target image and add a batch dimension
+    img = Transform(img).unsqueeze(0)
+    
+    # Put model into evaluation mode and turn on inference mode
+    result_0.eval()
+    with torch.inference_mode():
+        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
+        pred_probs = torch.softmax(result_0(img), dim=1)
+    
+    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
+    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+    
+    # Calculate the prediction time
+    pred_time = round(timer() - start_time, 5)
+    
+    # Return the prediction dictionary and prediction time 
+    return pred_labels_and_probs, pred_time
+
+### 4. Gradio app ###
+
+# Create title, description and article strings
+title = "Potato Leaves Detection AI"
+description = "Deep Learning model that classify what condition of Palm Leaves."
+article = "Created by LK."
+
+# Create examples list from "examples/" directory
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+# Create the Gradio demo
+demo = gr.Interface(fn=predict, # mapping function from input to output
+                    inputs=gr.Image(type="pil"), # what are the inputs?
+                    outputs=[gr.Label(num_top_classes=3, label="Predictions"), # what are the outputs?
+                             gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
+                    # Create examples list from "examples/" directory
+                    examples=example_list, 
+                    title=title,
+                    description=description,
+                    article=article,
+                    allow_flagging = 'never'
+                   )
+# Launch the demo!
+demo.launch()

model.py

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+import torch
+import torchvision
+
+from torch import nn
+import torchvision.models as models
+
+def ResNet18_model(num_classes:int=3):
+
+    # Create ResNet18 model
+    model_0 = models.resnet18(pretrained=True)
+    
+    # Get the length of class_names (one output unit for each class)
+    output_shape = num_classes
+
+    num_ftrs = model_0.fc.in_features
+
+    # Define the number of output classes for your task
+    num_classes = output_shape
+
+    # Replace the last linear layer with a new one that has the right number of output units
+    model_0.fc = torch.nn.Sequential(
+    torch.nn.Linear(num_ftrs, num_classes),
+    torch.nn.Dropout(p=0.2)
+    )
+    
+    return model_0

requirements.txt

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+torch
+torchvision
+gradio

transform.py

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+import torch
+from torchvision import transforms
+
+transforms_img = transforms.Compose([
+    
+    transforms.Resize((224, 224)), # 1. Reshape all images to 224x224 (though some models may require different sizes)
+    
+    transforms.ToTensor(), # 2. Turn image values to between 0 & 1 
+    
+    transforms.Normalize(mean=[0.1909, 0.1937, 0.1896], 
+                         std=[0.3242, 0.3258, 0.3336]) 
+])