Update app.py

app.py

@@ -10,7 +10,9 @@ import matplotlib.pyplot as plt
 import librosa.display
 from datetime import datetime
 import random
-from tensorflow.keras.preprocessing import image
+from tensorflow.keras.preprocessing import image 
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+
 import matplotlib
 
 
@@ -181,6 +183,8 @@ def model():
     # Upload an MP3 audio file
     with col1:
         audio_file = st.file_uploader('Upload an MP3 audio file', type=['mp3'])
+        dropdown = st.selectbox('Select Actual Bird Species', class_labels.values())
+
 
         if audio_file is not None:
             st.write('Processing...')
@@ -208,9 +212,14 @@ def model():
                 model = tf.keras.models.load_model(model_path)
 
                 # Load and preprocess the image
-                img = image.load_img(image_path, target_size=(224, 224))
-                img = image.img_to_array(img)
-                img = np.expand_dims(img, axis=0)
+                # Load the image directly from the BytesIO object
+                image = load_img(image_buffer, target_size=(224, 224))
+                
+                # Convert the image to a NumPy array
+                img_array = img_to_array(image)
+                
+                # Expand the dimensions to make it compatible with your model
+                img = np.expand_dims(img_array, axis=0)
 
                 # Make a prediction
                 predicted_class_index = model.predict(img, verbose=1)
@@ -218,6 +227,7 @@ def model():
                 predicted_class = class_labels[predicted_class_index.argmax()]
 
                 # Display the predicted bird species
+                st.write('Actual Bird Species:', dropdown)
                 st.write('Predicted Bird Species:', predicted_class)
 
 # create a Streamlit app