first commit

.gitattributes

@@ -33,3 +33,4 @@ 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
+plant_disease_classifier.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,39 @@
+import gradio as gr
+import numpy as np
+from PIL import Image
+from tensorflow.keras import models
+from tensorflow.keras.applications.efficientnet import preprocess_input
+
+# Step 1: Load the entire model (no need to manually load weights)
+model = models.load_model("plant_disease_classifier.keras")  # Load the saved model directly
+
+# Step 2: Define class names in the correct order
+class_names = ['Early Blight', 'Late Blight', 'Healthy']  # Ensure this matches the training order
+
+# Step 3: Define prediction function
+def predict(image: Image.Image):
+    image = image.convert("RGB")
+    image = image.resize((256, 256))
+    image_array = np.array(image, dtype=np.float32)
+    image_array = np.expand_dims(image_array, axis=0)
+    image_array = preprocess_input(image_array)
+
+    predictions = model.predict(image_array)
+    predicted_index = np.argmax(predictions)
+    confidence = float(predictions[0][predicted_index]) * 100
+    predicted_class = class_names[predicted_index]
+
+    return {predicted_class: confidence}
+
+# Step 4: Define Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title="Plant Disease Classifier",
+    description="Upload an image of a plant leaf to identify the disease."
+)
+
+# Step 5: Launch the app
+if __name__ == "__main__":
+    interface.launch()

plant_disease_classifier.keras

@@ -0,0 +1,3 @@
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+oid sha256:2799cf634baa82b2f16db4beef66dfd57d3f4b0b2ba0fd5e1563df7f4c202b98
+size 47916878