Update app.py

app.py

@@ -1,57 +1,58 @@
 import streamlit as st
+import cv2
 import numpy as np
 from tensorflow.keras.preprocessing import image
-from tensorflow.keras.models import load_model
+import tensorflow as tf
 
-# Define constants
-IMG_WIDTH, IMG_HEIGHT = 224, 224  # Adjust if your model requires different dimensions
-model_path = 'dog_cat_classifier_model.keras'  # Replace with your model path
+# Load the saved model (replace with your model filename)
+model = tf.keras.models.load_model('cat_dog_classifier.keras')
+
+# Image dimensions for the model
+img_width, img_height = 224, 224
+
+
+def preprocess_image(img):
+  """Preprocesses an image for prediction."""
+  img = cv2.resize(img, (img_width, img_height))
+  img = img.astype('float32') / 255.0
+  img = np.expand_dims(img, axis=0)
+  return img
+
+
+def predict_class(image):
+  """Predicts image class and probabilities."""
+  preprocessed_img = preprocess_image(image)
+  prediction = model.predict(preprocessed_img)
+  class_names = ['cat', 'dog']  # Adjust class names according to your model
+  return class_names[np.argmax(prediction)], np.max(prediction)
+
+
+def display_results(class_name, probability):
+  """Displays prediction results in a progress bar style."""
+  st.write(f"**Predicted Class:** {class_name}")
+
+  # Create a progress bar using st.progress
+  progress = st.progress(0)
+  for i in range(100):
+    progress.progress(i + 1)
+    if i == int(probability * 100):
+      break
+  st.write(f"**Probability:** {probability:.2f}")
 
-# Load the model (outside the main app function for efficiency)
-model = load_model(model_path)
 
 def main():
-    """
-    Streamlit app for image classification with user-friendly interface.
-    """
-
-    # Title and description
-    st.title("Intriguing Image Classifier")
-    st.write("Upload an image and discover its most likely category along with probabilities in a compelling way!")
-
-    # File uploader and sidebar for image selection
-    uploaded_file = st.file_uploader("Choose an Image", type=['jpg', 'jpeg', 'png'])
-    image_selected = st.sidebar.selectbox("Select Image", (None, "Uploaded Image"))
-
-    if uploaded_file is not None:
-        image_display = image.load_img(uploaded_file, target_size=(IMG_WIDTH, IMG_HEIGHT))
-        st.image(image_display, caption="Uploaded Image", use_column_width=True)
-        image_selected = "Uploaded Image"
-
-    # Preprocess image if one is selected
-    if image_selected:
-        img_array = image.img_to_array(image_display)
-        img_array = np.expand_dims(img_array, axis=0)
-        img_array /= 255.0  # Rescale pixel values to [0, 1]
-
-        # Make predictions and get class labels (assuming your model outputs probabilities)
-        predictions = model.predict(img_array)
-        class_labels = [f"{label}: {prob:.2%}" for label, prob in zip(get_class_labels(model), predictions[0])]
-
-        # Display predictions in an intriguing way (replace with your preferred method)
-        st.header("Predictions:")
-        progress_bar_width = 800  # Adjust for desired visual style
-
-        for label, prob in zip(class_labels, predictions[0]):
-            progress_bar = st.progress(label)
-            progress_bar.progress(int(prob * 100))  # Update progress bar based on probability
-
-# Function to retrieve class labels from the model (replace if your model structure is different)
-def get_class_labels(model):
-    class_names = list(model.class_names)  # Assuming class names are directly accessible
-    return class_names
+  """Main app function."""
+  st.title("Image Classifier")
+  st.write("Upload an image to classify it as cat or dog.")
 
-main()
+  uploaded_file = st.file_uploader("Choose an image...", type="jpg")
+  if uploaded_file is not None:
+    image = cv2.imdecode(np.frombuffer(uploaded_file.read(), np.uint8), cv2.IMREAD_COLOR)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
 
+    predicted_class, probability = predict_class(image)
+    display_results(predicted_class, probability)
+
+main()
 # if __name__ == '__main__':
-#     main()
+#   main()