Create rice_leaf_disease.py

rice_leaf_disease.py

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+import gradio as gr
+import spaces
+from transformers import AutoImageProcessor, SiglipForImageClassification
+from transformers.image_utils import load_image
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/Rice-Leaf-Disease"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+@spaces.GPU
+def classify_leaf_disease(image):
+    """Predicts the disease type in a rice leaf image."""
+    image = Image.fromarray(image).convert("RGB")
+    inputs = processor(images=image, return_tensors="pt")
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
+    
+    labels = {
+        "0": "Bacterial Blight",
+        "1": "Blast",
+        "2": "Brown Spot",
+        "3": "Healthy",
+        "4": "Tungro"
+    }
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=classify_leaf_disease,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Rice Leaf Disease Classification 🌾",
+    description="Upload an image of a rice leaf to identify if it is healthy or affected by diseases like Bacterial Blight, Blast, Brown Spot, or Tungro."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()