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app.py

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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', use_column_width=True, 
+             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)
+

cnn_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9761bd08070d88b40efbfa051cd064d4682aef744d71d9be9e5c6b67fa569a2b
+size 96309296

requirements.txt

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+streamlit
+tensorflow
+Pillow
+matplotlib
+seaborn