Update app.py

app.py

@@ -1,50 +1,50 @@
-import numpy as np
-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 = '/content/my_cnn.h5'  # or '/content/my_model.keras'
-model = load_model(model_path)
-
-# Preprocess the image
-def preprocess_image(img):
-    img_array = image.img_to_array(img)
-    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
-    img_array = preprocess_input(img_array)  # Ensure correct preprocessing for ResNet50
-    return img_array
-
-# Make predictions and map to class labels
-def classify_image(img):
-    img_array = preprocess_image(img)
-    predictions = model.predict(img_array)
-    predicted_class = np.argmax(predictions, axis=1)  # Get the index of the highest probability
-
-    class_labels = {0: 'Aedes Aegypti', 1: 'Anopheles Stephensi', 2: 'Culex Quinquefasciatus'}
-    species = class_labels.get(predicted_class[0], "Unknown")
-    
-    return species, predictions
-
-# Streamlit application
-def main():
-    st.title("Mosquito Species Classification")
-    st.write("Upload a mosquito image to classify its species.")
-
-    # File uploader for image input
-    uploaded_file = st.file_uploader("Choose an 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)
-        
-        # Classify the image
-        result, probabilities = classify_image(img)
-        st.write(f'Predicted mosquito species: **{result}**')
-        st.write(f'Prediction probabilities: {probabilities}')
-
-# Run the app
-if __name__ == "__main__":
-    main()
+import numpy as np
+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'
+model = load_model(model_path)
+
+# Preprocess the image
+def preprocess_image(img):
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    img_array = preprocess_input(img_array)  # Ensure correct preprocessing for ResNet50
+    return img_array
+
+# Make predictions and map to class labels
+def classify_image(img):
+    img_array = preprocess_image(img)
+    predictions = model.predict(img_array)
+    predicted_class = np.argmax(predictions, axis=1)  # Get the index of the highest probability
+
+    class_labels = {0: 'Aedes Aegypti', 1: 'Anopheles Stephensi', 2: 'Culex Quinquefasciatus'}
+    species = class_labels.get(predicted_class[0], "Unknown")
+    
+    return species, predictions
+
+# Streamlit application
+def main():
+    st.title("Mosquito Species Classification")
+    st.write("Upload a mosquito image to classify its species.")
+
+    # File uploader for image input
+    uploaded_file = st.file_uploader("Choose an 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)
+        
+        # Classify the image
+        result, probabilities = classify_image(img)
+        st.write(f'Predicted mosquito species: **{result}**')
+        st.write(f'Prediction probabilities: {probabilities}')
+
+# Run the app
+if __name__ == "__main__":
+    main()