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import streamlit as st
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Load the trained model
model = load_model('cnn_model.h5')
# Function to process the uploaded image
def process_image(img):
img = img.convert('RGB')
img = img.resize((32, 32))
img = np.array(img)
img = img / 255.0
img = np.expand_dims(img, axis=0)
return img
# Frontend design
st.set_page_config(page_title="Dog vs Cat Detection", page_icon="🐶🐱", layout="centered")
st.title("Dog vs Cat Image Classification 🐶🐱")
# Description
st.markdown("""
This is a simple Dog vs Cat image classifier. Upload an image of either a dog or a cat, and
the model will predict the class along with the confidence level.
""")
# Image upload
file = st.file_uploader('Select an image', type=['jpg', 'jpeg', 'png'])
if file is not None:
img = Image.open(file)
# Display the uploaded image with a border and centered
st.image(img, caption='Uploaded Image',
output_format="PNG", width=400)
# Preprocess the image
image = process_image(img)
# Model prediction
with st.spinner('Classifying the image...'):
predictions = model.predict(image)
predicted_class = np.argmax(predictions)
predicted_prob = predictions[0][predicted_class]
# Class names for prediction
class_names = ['Cat','Dog']
# Display the prediction result
st.subheader(f"Prediction: {class_names[predicted_class]}")
st.write(f"Confidence: {predicted_prob * 100:.2f}%")
# Display prediction probabilities
st.write("Prediction Probabilities for Each Class:")
# Prepare probabilities for visualization
probabilities = predictions[0]
prob_dict = {class_names[i]: probabilities[i] for i in range(len(class_names))}
# Plot settings
sns.set(style="whitegrid") # Use a grid style for the plot
# Create the figure for the bar chart
fig, ax = plt.subplots(figsize=(10, 6)) # Adjust figure size for better readability
# Plot the bar chart with a brighter color palette
ax.bar(list(prob_dict.keys()), list(prob_dict.values()), color='#f5a623', edgecolor='black')
ax.set_ylabel('Probability', fontsize=14, color='black')
ax.set_title('Prediction Probabilities for Each Class', fontsize=18, color='black')
# Rotate x-axis labels for better readability
plt.xticks(rotation=45, ha='right', fontsize=12)
# Annotate bars with percentage values
for index, value in enumerate(prob_dict.values()):
ax.text(index, value, f'{value * 100:.0f}%', va='bottom', ha='center', fontsize=10)
# Style improvements: Remove background grid and spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.grid(False)
# Adjust layout to prevent clipping
fig.tight_layout()
# Display the plot in Streamlit
st.pyplot(fig)
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