updated the UI

src/streamlit_app.py

@@ -1,5 +1,5 @@
 import streamlit as st
-st.set_page_config(page_title="PinoyPaws", layout="centered")
+st.set_page_config(page_title="PinoyPaws", layout="wide")
 
 import tensorflow as tf
 from PIL import Image
@@ -8,6 +8,17 @@ import os
 import json
 from tensorflow.keras.models import load_model
 from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
+from tensorflow.keras.utils import plot_model
+import io
+
+# === Page Configuration ===
+st.sidebar.title("📍 PinoyPaws Navigation")
+
+# === Sidebar Navigation ===
+page = st.sidebar.selectbox("Navigate to", ["Overview", "Predict Breed", "Model Insights"])
+
+with st.sidebar.expander("ℹ️ About this App"):
+    st.markdown("Built with 🐍 TensorFlow and 🧠 MobileNetV2")
 
 # === Load model ===
 @st.cache_resource
@@ -33,29 +44,75 @@ def preprocess_image(image: Image.Image) -> np.ndarray:
     image_array = np.array(image)
     if image_array.shape[-1] == 4:
         image_array = image_array[..., :3]
-    image_array = preprocess_input(image_array)  # Important!
+    image_array = preprocess_input(image_array)
     return np.expand_dims(image_array, axis=0)
 
-# === Streamlit UI ===
-st.title("🐾 PinoyPaws: Dog Breed Classifier")
-st.write(f"Upload an image of a dog and let the model predict its breed from {len(class_names)} common dog breeds.")
+# === Page: Overview ===
+if page == "Overview":
+    st.title("🐾 PinoyPaws: Dog Breed Classifier")
+    st.markdown("""
+    Welcome to **PinoyPaws**, a dog breed classifier tailored to recognize common dog breeds found in the Philippines 🐕🇵🇭.
+
+    ### 📌 Features:
+    - 📷 Upload a dog image and let our AI guess the breed!
+    - 🧠 Built using **MobileNetV2** for fast and lightweight inference
+    - 📊 Confidence score included
+    - 🐕 Trained on 5 local and common breeds:
+                - **Beagle**
+                - **Chihuahua**
+                - **Golden Retriever**
+                - **Shih Tzu**
+                - **Siberian Husky**
+
+    ### 📁 Input:
+    - Accepts `.jpg`, `.jpeg`, `.png` images
+    - Optimized for images where the dog is clearly visible
+
+    You can get started by choosing **Predict Breed** in the sidebar.
+    """)
+
+# === Page: Predict Breed ===
+elif page == "Predict Breed":
+    st.title("🔎🐶 Predict Dog Breed")
+    st.write(f"Upload an image of a dog and let the model predict its breed from {len(class_names)} common dog breeds.")
+
+    uploaded_file = st.file_uploader("📷 Choose a dog image...", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+            image = Image.open(uploaded_file).convert("RGB")
+            st.image(image, caption="Uploaded Image", use_container_width=True)
+
+            if st.button("🖼️ Predict"):
+
+                with st.spinner("Classifying..."):
+                    try:
+
+                        input_tensor = preprocess_image(image)
+                        prediction = model.predict(input_tensor)
+                        predicted_index = int(np.argmax(prediction))
+                        predicted_class = class_names[predicted_index]
+                        confidence = np.max(prediction)
+
+                        st.success(f"🐶 Predicted Breed: **{predicted_class}**")
+                        st.info(f"📊 Confidence: {confidence * 100:.2f}%")
 
-uploaded_file = st.file_uploader("📷 Choose a dog image...", type=["jpg", "jpeg", "png"])
+                    except Exception as e:
+                        st.error(f"An error occurred: {e}")
 
-if uploaded_file is not None:
-    try:
-        image = Image.open(uploaded_file).convert("RGB")
-        st.image(image, caption="Uploaded Image", use_container_width=True)
+# === Page: Model Insights ===
+elif page == "Model Insights":
+    st.title("📊 Model Insights & Architecture")
 
-        with st.spinner("Classifying..."):
-            input_tensor = preprocess_image(image)
-            prediction = model.predict(input_tensor)
-            predicted_index = int(np.argmax(prediction))
-            predicted_class = class_names[predicted_index]
-            confidence = np.max(prediction)
+    st.markdown("### 🧠 Model Summary")
+    string_io = io.StringIO()
+    model.summary(print_fn=lambda x: string_io.write(x + "\n"))
+    summary_str = string_io.getvalue()
+    st.text(summary_str)
 
-        st.success(f"🐶 Predicted Breed: **{predicted_class}**")
-        st.info(f"📊 Confidence: {confidence * 100:.2f}%")
+    st.markdown("### 🧬 Model Details")
+    st.write(f"• Total parameters: `{model.count_params():,}`")
+    st.write("• Architecture: **MobileNetV2** base with custom dense layers")
 
-    except Exception as e:
-        st.error(f"An error occurred: {e}")
+    st.markdown("### 📚 Classes Detected")
+    st.write(f"The model can classify the following {len(class_names)} breeds:")
+    st.markdown(" - " + "\n - ".join(class_names))