Update app.py

app.py

@@ -1,6 +1,65 @@
+# import streamlit as st
+# # import train
+# # import tr
+# # import prd
+# x = st.slider('Select a value')
+# st.write(x, 'squared is', x * 86)
 import streamlit as st
-# import train
-# import tr
-import prd
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * 86)
+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/catsanddogs")
+
+# 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}")
+
+
+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)
+
+    predicted_class, probability = predict_class(image)
+    display_results(predicted_class, probability)
+
+main()