Create app.py

app.py

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+# app.py
+import streamlit as st
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+# Set page config
+st.set_page_config(page_title="Rice Disease Classifier", page_icon="🌾")
+
+# Constants from your training
+IMG_SIZE = (224, 224)
+CLASS_NAMES = ['Bacterial_leaf_blight', 'Brown_spot', 'Healthy', 'Leaf_blast']
+
+# Cache the model loading
+@st.cache_resource
+def load_model():
+    return tf.keras.models.load_model('rice_disease_model.keras')
+
+# Load model
+try:
+    model = load_model()
+except Exception as e:
+    st.error(f"Error loading model: {str(e)}")
+    st.stop()
+
+# Preprocessing function
+def preprocess_image(image):
+    image = image.resize(IMG_SIZE)
+    img_array = tf.keras.utils.img_to_array(image)
+    img_array = tf.expand_dims(img_array, 0)  # Create batch axis
+    img_array = tf.keras.applications.mobilenet_v2.preprocess_input(img_array)
+    return img_array
+
+# Streamlit interface
+st.title("Rice Disease Classifier 🌾")
+st.write("Upload an image of a rice leaf for disease diagnosis")
+
+uploaded_file = st.file_uploader("Choose an image...", 
+                               type=["jpg", "jpeg", "png", "webp"])
+
+if uploaded_file is not None:
+    try:
+        # Read and display image
+        image = Image.open(uploaded_file).convert('RGB')
+        st.image(image, caption="Uploaded Image", use_container_width=True)  # Fixed parameter here
+
+        # Preprocess and predict
+        with st.spinner('Analyzing...'):
+            processed_image = preprocess_image(image)
+            predictions = model.predict(processed_image)
+            predicted_class = CLASS_NAMES[np.argmax(predictions[0])]
+            confidence = np.max(predictions[0]) * 100
+
+        # Display results
+        st.subheader("Results")
+        st.success(f"Predicted Disease: **{predicted_class}**")
+        st.info(f"Confidence: **{confidence:.2f}%**")
+
+        # Show probability distribution
+        st.subheader("Class Probabilities")
+        for class_name, prob in zip(CLASS_NAMES, predictions[0]):
+            st.progress(float(prob), text=f"{class_name}: {prob*100:.2f}%")
+
+    except Exception as e:
+        st.error(f"Error processing image: {str(e)}")