upload code and model

app.py

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+import gradio as gr
+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+from torchvision.models import ResNet18_Weights
+
+# Load the saved model
+model = models.resnet18(weights=ResNet18_Weights.IMAGENET1K_V1)
+model.fc = nn.Linear(model.fc.in_features, 1000)  # Adjust to match the original model's output units
+model.load_state_dict(torch.load('grass_wood_classification_model.pth'))
+model.eval()
+
+# Create a new model with the correct final layer
+new_model = models.resnet18(weights=ResNet18_Weights.IMAGENET1K_V1)
+new_model.fc = nn.Linear(new_model.fc.in_features, 2)  # Adjust to match the desired output units
+
+# Copy the weights and biases from the loaded model to the new model
+new_model.fc.weight.data = model.fc.weight.data[0:2]  # Copy only the first 2 output units
+new_model.fc.bias.data = model.fc.bias.data[0:2]
+
+# Define the preprocessing function
+def preprocess_image(image):
+    preprocess = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+    input_tensor = preprocess(image)
+    input_batch = input_tensor.unsqueeze(0)  # Add a batch dimension
+    return input_batch
+
+# Define the prediction function
+def predict(image):
+    input_batch = preprocess_image(image)
+    new_model.eval()
+    with torch.no_grad():
+        output = new_model(input_batch)
+    _, predicted_class = output.max(1)
+    class_names = ['grass', 'wood']
+    predicted_class_name = class_names[predicted_class.item()]
+    return predicted_class_name
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type='pil', label="Upload an Image"),  # Use 'pil' to match the input type
+    outputs="text",
+    title="Grass or Wood Classifier Using ResNet18",
+    description="Upload an image to classify it as either grass or wood."
+)
+
+# Launch the interface
+demo.launch(share=True)

grass_wood_classification_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:52c0507644bb63b668eb8ef00c0fa55a6a5e20811088274be049551fac2aa78f
+size 46838286