Update app.py

app.py

@@ -4,4 +4,41 @@ os.environ["KERAS_BACKEND"] = "jax"
 	
 import keras
 
-model = keras.saving.load_model("hf://Fluospark128/Cassava_Disease_Classifier")
+#model = keras.saving.load_model("hf://Fluospark128/Cassava_Disease_Classifier")
+
+import streamlit as st
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load the model
+@st.cache_resource
+def load_model():
+    model = keras.saving.load_model("hf://Fluospark128/Cassava_Disease_Classifier") #model = tf.keras.models.load_model("cassava_model.h5")
+    return model
+
+model = load_model()
+
+# Class labels
+CLASS_NAMES = ["Cassava Bacterial Blight", "Cassava Mosaic Disease", "Cassava Brown Streak Disease", "Healthy", "Cassava Green Mottle"]
+
+# Streamlit UI
+st.title("Cassava Leaf Disease Classifier")
+st.write("Upload an image of a cassava leaf to classify its disease.")
+
+# File uploader
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file).resize((224, 224))
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Preprocess image
+    img_array = np.array(image) / 255.0  # Normalize
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+
+    # Make prediction
+    prediction = model.predict(img_array)
+    predicted_class = CLASS_NAMES[np.argmax(prediction)]
+
+    st.write(f"Prediction: **{predicted_class}**")