Delete rice_leaf_disease.py

rice_leaf_disease.py

@@ -1,46 +0,0 @@
-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()