Update app.py

app.py

@@ -3,7 +3,6 @@ import streamlit as st
 from tensorflow.keras.preprocessing import image
 from tensorflow.keras.models import load_model
 from tensorflow.keras.applications.resnet50 import preprocess_input
-import matplotlib.pyplot as plt
 
 # Load the trained model
 model_path = 'my_cnn.h5'  # or '/content/my_model.keras'
@@ -30,15 +29,35 @@ def classify_image(img):
 # Streamlit application
 def main():
     st.title("Mosquito Species Classification")
-    st.write("Upload a mosquito image to classify its species.")
+    st.write("Upload a mosquito image or select an example image to classify its species.")
+
+    # Example images
+    example_images = {
+        "Aedes Aegypti": "Aedes_aegypti_1_0_832.jpg",
+        "Anopheles Stephensi": "Anopheles_stephensi_1_0_364.jpg",
+        "Culex Quinquefasciatus": "Culex_quinquefasciatus_1_0_1307.jpg",
+    }
+
+    # Select an example image
+    selected_example = st.selectbox("Or select an example image:", list(example_images.keys()))
+    
+    if selected_example:
+        img_path = example_images[selected_example]
+        img = image.load_img(img_path, target_size=(224, 224))
+        st.image(img, caption=f'Selected Example Image: {selected_example}', width=224)
+        
+        # Classify the example image
+        result, probabilities = classify_image(img)
+        st.write(f'Predicted mosquito species: **{result}**')
+        st.write(f'Prediction probabilities: {probabilities}')
 
     # File uploader for image input
-    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+    uploaded_file = st.file_uploader("Or upload your own image...", type=["jpg", "jpeg", "png"])
 
     if uploaded_file is not None:
         # Load the image for display
         img = image.load_img(uploaded_file, target_size=(224, 224))
-        st.image(img, caption='Uploaded Image', use_column_width=True)
+        st.image(img, caption='Uploaded Image', width=224)
         
         # Classify the image
         result, probabilities = classify_image(img)