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import streamlit as st
import numpy as np
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import load_model
# 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 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()
# if __name__ == '__main__':
# main()
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