Update app.py

app.py

@@ -1,49 +1,7 @@
-import streamlit as st
 import tensorflow as tf
-import numpy as np
-from PIL import Image
-import io
-import json
 
-# Load categories
-with open('categories.json') as f:
-    categories = json.load(f)
-
-# Ensure the correct path
-model_path = 'plant_disease_detection_model.h5'
-
-# Load the TensorFlow model
 try:
-    model = tf.keras.models.load_model(model_path)
-except FileNotFoundError:
-    st.error(f"Model file not found: {model_path}")
-    st.stop()
-
-def preprocess_image(image):
-    # Convert the image to a NumPy array
-    image = Image.open(io.BytesIO(image))
-    image = image.resize((224, 224))  # Adjust size as needed
-    image_array = np.array(image) / 255.0  # Normalize to [0, 1]
-    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
-    return image_array
-
-def main():
-    st.title("Plant Disease Detection")
-    uploaded_file = st.file_uploader("Choose an image...", type="jpg")
-    if uploaded_file is not None:
-        # Preprocess image
-        image_array = preprocess_image(uploaded_file.read())
-
-        # Make prediction
-        predictions = model.predict(image_array)
-        predicted_class = np.argmax(predictions, axis=1)[0]
-        confidence = float(predictions[0][predicted_class])
-
-        # Map to category names
-        predicted_label = categories.get(str(predicted_class), 'Unknown')
-
-        st.write(f"Prediction: {predicted_label}")
-        st.write(f"Confidence: {confidence:.2f}")
-
-if __name__ == "__main__":
-    main()
+    model = tf.keras.models.load_model('plant_disease_detection_model.h5')
+    print("Model loaded successfully")
+except Exception as e:
+    print(f"Error loading model: {e}")