Update app.py

app.py

@@ -1,67 +1,67 @@
-# import streamlit as st
-# import train
-# # import tr
-# # import prd
-# x = st.slider('Select a value')
-# st.write(x, 'squared is', x * 86)
-# st.write(x, 'squared is', x * 86)
-
 import streamlit as st
-import cv2
-import numpy as np
-from tensorflow.keras.preprocessing import image
-import tensorflow as tf
-from huggingface_hub import from_pretrained_keras
+import train
+# import tr
+# import prd
+x = st.slider('Select a value')
+st.write(x, 'squared is', x * 86)
+st.write(x, 'squared is', x * 86)
+
+# import streamlit as st
+# import cv2
+# import numpy as np
+# from tensorflow.keras.preprocessing import image
+# import tensorflow as tf
+# from huggingface_hub import from_pretrained_keras
 
-model = from_pretrained_keras("okeowo1014/catsanddogsmodel")
+# model = from_pretrained_keras("okeowo1014/catsanddogsmodel")
 
-# Load the saved model (replace with your model filename)
-# model = tf.keras.models.load_model('cat_dog_classifier.keras')
+# # 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
+# # 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 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 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}")
+# 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}")
+#   # 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}")
 
 
-def main():
-  """Main app function."""
-  st.title("Image Classifier")
-  st.write("Upload an image to classify it as cat or dog.")
+# def main():
+#   """Main app function."""
+#   st.title("Image Classifier")
+#   st.write("Upload an image to classify it as cat or dog.")
 
-  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)
+#   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)
+#     predicted_class, probability = predict_class(image)
+#     display_results(predicted_class, probability)
 
-main()
+# main()