Update app.py

app.py

@@ -1,28 +1,35 @@
 import streamlit as st
 import numpy as np
-from keras.models import load_model
-from PIL import Image
-from tensorflow.keras.applications.resnet50 import preprocess_input
-
-
-model = load_model('./best_weights.hdf5')
-
-st.title('Respiratory Symptom Detection')
-
-uploaded_file = st.file_uploader("Choose an image: ", type="jpg")
-
-if uploaded_file is not None:
-    img = Image.open(uploaded_file)
-    img = img.resize((224, 224))  
-    img_array = np.array(img)
-    
-    # If your model expects a 3-channel RGB image, expand dimensions like this:
-    img_array = np.expand_dims(img_array, axis=0)  # Adding batch dimension
-    image_preprocessed = preprocess_input(img_array)
-    
-    if st.button('Predict'):
-        prediction = model.predict(image_preprocessed)
-        predicted_class_index = np.argmax(prediction)
-        class_labels = {0: 'COVID19', 1: 'NORMAL', 2: 'PNEUMONIA', 3: 'TUBERCULOSIS'}
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+from tensorflow.keras.applications.densenet import preprocess_input
+from tensorflow.keras.models import load_model
+
+# Load the pre-trained model
+model = load_model("./best_weights.hdf5")  # Replace with the path to your model
+
+class_labels = {0: 'COVID19', 1: 'NORMAL', 2: 'PNEUMONIA', 3: 'TURBERCULOSIS'}
+
+def main():
+    st.title("Respiratory Symptom Prediction")
+    st.write("Upload an image to predict the respiratory symptom.")
+
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        # Load and preprocess the uploaded image
+        image = load_img(uploaded_file, target_size=(224, 224))
+        image_array = img_to_array(image)
+        image_preprocessed = preprocess_input(image_array)
+        image_batch = np.expand_dims(image_preprocessed, axis=0)
+
+        # Make predictions
+        predictions = model.predict(image_batch)
+        predicted_class_index = np.argmax(predictions)
         predicted_class_label = class_labels[predicted_class_index]
-        st.write(predicted_class_label)
+
+        # Display the uploaded image and prediction result
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+        st.write(f"Predicted class label: {predicted_class_label}")
+
+if __name__ == "__main__":
+    main()