Update app.py

app.py

@@ -1,13 +1,8 @@
 import streamlit as st
-import tensorflow as tf
 from tensorflow.keras.models import load_model
 from tensorflow.keras.preprocessing.image import load_img, img_to_array
 import numpy as np
 import pickle
-import concurrent.futures
-
-# Ensure TensorFlow is installed
-# !pip install tensorflow
 
 # Load the saved model
 model = load_model("best_model.h5")
@@ -32,14 +27,9 @@ def predict_image(image):
     predicted_label = class_indices[predicted_class]
     return predicted_label
 
-# Function to handle predictions asynchronously
-def async_predict(image):
-    with concurrent.futures.ThreadPoolExecutor() as executor:
-        future = executor.submit(predict_image, image)
-        return future.result()
-
 # Streamlit App
 st.title("Rice Leaf Disease Classification")
+
 st.write("Upload an image of a rice leaf and the model will predict its disease category.")
 
 # File uploader
@@ -52,7 +42,6 @@ if uploaded_file is not None:
     st.write("")
     st.write("Classifying...")
 
-    # Make a prediction asynchronously
-    with st.spinner('Wait for it...'):
-        predicted_label = async_predict(uploaded_file)
-        st.write(f"Predicted label: {predicted_label}")
+    # Make a prediction
+    predicted_label = predict_image(uploaded_file)
+    st.write(f"Predicted label: {predicted_label}")