Added more class labels in test 3.
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+21
-5
@@ -8,15 +8,31 @@ model_name = "TonyStarkD99/CLIP-Crop_Disease-Large"
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model = CLIPModel.from_pretrained(model_name)
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# Load your image
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image_path = "/home/overnion/Status200/potato.png" # Replace with your image path
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image_path = "/home/overnion/Status200/tomato.png" # Replace with your image path
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image = Image.open(image_path)
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# Define the class labels (text prompts)
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class_labels = [
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"healthy plant",
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"diseased plant",
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"wilted plant",
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"pest-infested plant"
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"powdery mildew",
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"leaf rust",
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"stem rust",
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"fusarium head blight",
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"gray leaf spot",
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"bacterial blight",
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"downy mildew",
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"aphid infestation",
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"white mold",
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"black rot",
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"root rot",
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"yellow leaf curl",
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"blight",
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"necrotic spots",
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"chlorosis",
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"wilt",
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"damping off",
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"viral infection",
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"pest damage"
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]
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# Resize and normalize the image
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@@ -46,5 +62,5 @@ predicted_class = class_labels[predicted_class_idx]
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# Print the predicted class and probabilities
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print("Predicted class:", predicted_class)
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print("Probabilities:", probs.detach().numpy())
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# print("Probabilities:", probs.detach().numpy())
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