deploy

.gitignore

@@ -0,0 +1,2 @@
+.env
+venv/

README.md

@@ -1,14 +1,14 @@
 ---
 title: PinoyPaws
-emoji: 🚀
-colorFrom: red
-colorTo: red
+emoji: 🐾
+colorFrom: green
+colorTo: yellow
 sdk: docker
 app_port: 8501
 tags:
 - streamlit
 pinned: false
-short_description: A CNN-Based Dog Breed Classifier using the Stanford Dogs Dat
+short_description: A CNN-Based Dog Breed Classifier using the Stanford Dogs DS
 license: mit
 ---
 

requirements.txt

@@ -1,3 +1,5 @@
-altair
-pandas
-streamlit
+streamlit
+tensorflow
+Pillow
+numpy
+matplotlib

src/model/class_names.json

@@ -0,0 +1 @@
+["Beagle", "Chihuahua", "Golden Retriever", "Shih-Tzu", "Siberian Husky"]

src/model/dog_breed_classifier.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1907674b38e8b2f5afbe702e826272939fa5a5762a410c615ee735dae1358709
+size 18666336

src/streamlit_app.py

@@ -1,40 +1,61 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+st.set_page_config(page_title="PinoyPaws", layout="centered")
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+import os
+import json
+from tensorflow.keras.models import load_model
+from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
+
+# === Load model ===
+@st.cache_resource
+def load_model():
+    model_path = os.path.join("src", "model", "dog_breed_classifier.h5")
+    model = tf.keras.models.load_model(model_path)
+    return model
+
+model = load_model()
+
+# === Load class names ===
+@st.cache_data
+def load_class_names():
+    labels_path = os.path.join("src", "model", "class_names.json")
+    with open(labels_path, "r") as f:
+        return json.load(f)
+
+class_names = load_class_names()
+
+# === Preprocess image ===
+def preprocess_image(image: Image.Image) -> np.ndarray:
+    image = image.resize((224, 224))
+    image_array = np.array(image)
+    if image_array.shape[-1] == 4:
+        image_array = image_array[..., :3]
+    image_array = preprocess_input(image_array)  # Important!
+    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.")
+
+uploaded_file = st.file_uploader("📷 Choose a dog image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    try:
+        image = Image.open(uploaded_file).convert("RGB")
+        st.image(image, caption="Uploaded Image", use_container_width=True)
+
+        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.success(f"🐶 Predicted Breed: **{predicted_class}**")
+        st.info(f"📊 Confidence: {confidence * 100:.2f}%")
+
+    except Exception as e:
+        st.error(f"An error occurred: {e}")

train_model.py

@@ -0,0 +1,156 @@
+import os
+import json
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image_dataset_from_directory
+from tensorflow.keras.applications import MobileNetV2  # Lightweight & effective for small datasets
+from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout, BatchNormalization, Rescaling
+from tensorflow.keras.models import Model, Sequential
+from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
+from tensorflow.keras.optimizers import Adam, SGD
+import matplotlib.pyplot as plt
+
+# === Paths ===
+DATA_DIR = "data/train"
+MODEL_SAVE_PATH = "src/model/dog_breed_classifier.h5"
+CLASS_NAMES_PATH = "src/model/class_names.json"
+IMG_SIZE = (224, 224)
+BATCH_SIZE = 32
+SEED = 42
+
+# === Load dataset ===
+print("[INFO] Loading dataset...")
+train_ds = image_dataset_from_directory(
+    DATA_DIR,
+    validation_split=0.2,
+    subset="training",
+    seed=SEED,
+    image_size=IMG_SIZE,
+    batch_size=BATCH_SIZE
+)
+
+val_ds = image_dataset_from_directory(
+    DATA_DIR,
+    validation_split=0.2,
+    subset="validation",
+    seed=SEED,
+    image_size=IMG_SIZE,
+    batch_size=BATCH_SIZE
+)
+
+# Save class names for inference
+class_names = train_ds.class_names
+num_classes = len(class_names)
+print(f"[INFO] Classes found: {num_classes}")
+
+with open(CLASS_NAMES_PATH, "w") as f:
+    json.dump(class_names, f)
+
+# === Data preprocessing & augmentation ===
+resize_and_rescale = Sequential([
+    Rescaling(1./255)
+])
+
+data_augmentation = Sequential([
+    tf.keras.layers.RandomFlip("horizontal"),
+    tf.keras.layers.RandomRotation(0.15),
+    tf.keras.layers.RandomZoom(0.1)
+])
+
+AUTOTUNE = tf.data.AUTOTUNE
+train_ds = train_ds.map(lambda x, y: (resize_and_rescale(x), y))
+train_ds = train_ds.map(lambda x, y: (data_augmentation(x, training=True), y))
+train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE)
+
+val_ds = val_ds.map(lambda x, y: (resize_and_rescale(x), y))
+val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
+
+# === Compute class weights (to handle class imbalance) ===
+print("[INFO] Computing class weights...")
+y_train = np.concatenate([y.numpy() for _, y in train_ds], axis=0)
+class_counts = np.bincount(y_train)
+total = len(y_train)
+class_weights = {i: total / (num_classes * count) for i, count in enumerate(class_counts)}
+print("[INFO] Class weights applied.")
+
+# === Build model ===
+print("[INFO] Building model...")
+base_model = MobileNetV2(input_shape=IMG_SIZE + (3,), include_top=False, weights='imagenet')
+base_model.trainable = False
+
+x = base_model.output
+x = GlobalAveragePooling2D()(x)
+x = BatchNormalization()(x)
+x = Dropout(0.4)(x)
+output = Dense(num_classes, activation='softmax')(x)
+
+model = Model(inputs=base_model.input, outputs=output)
+model.compile(
+    optimizer=Adam(learning_rate=1e-4),
+    loss='sparse_categorical_crossentropy',
+    metrics=['accuracy']
+)
+
+model.summary()
+
+# === Callbacks ===
+os.makedirs(os.path.dirname(MODEL_SAVE_PATH), exist_ok=True)
+checkpoint = ModelCheckpoint(MODEL_SAVE_PATH, monitor='val_loss', save_best_only=True, verbose=1)
+earlystop = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
+reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.3, patience=2, verbose=1)
+
+# === Phase 1: Train head ===
+print("[INFO] Training model (frozen base)...")
+history = model.fit(
+    train_ds,
+    validation_data=val_ds,
+    epochs=15,
+    class_weight=class_weights,
+    callbacks=[checkpoint, earlystop, reduce_lr]
+)
+
+# === Phase 2: Fine-tune full model ===
+print("[INFO] Fine-tuning entire model...")
+base_model.trainable = True
+
+model.compile(
+    optimizer=SGD(learning_rate=1e-4, momentum=0.9),
+    loss='sparse_categorical_crossentropy',
+    metrics=['accuracy']
+)
+
+fine_tune_epochs = 10
+total_epochs = len(history.history["loss"]) + fine_tune_epochs
+
+fine_tune_history = model.fit(
+    train_ds,
+    validation_data=val_ds,
+    epochs=total_epochs,
+    initial_epoch=history.epoch[-1] + 1,
+    class_weight=class_weights,
+    callbacks=[checkpoint, earlystop, reduce_lr]
+)
+
+# === Merge histories ===
+for key in fine_tune_history.history:
+    history.history[key] += fine_tune_history.history[key]
+
+# === Plot training results ===
+plt.figure(figsize=(12, 4))
+
+plt.subplot(1, 2, 1)
+plt.plot(history.history['loss'], label='Train Loss')
+plt.plot(history.history['val_loss'], label='Val Loss')
+plt.title("Loss")
+plt.legend()
+
+plt.subplot(1, 2, 2)
+plt.plot(history.history['accuracy'], label='Train Acc')
+plt.plot(history.history['val_accuracy'], label='Val Acc')
+plt.title("Accuracy")
+plt.legend()
+
+plt.savefig("training_curves.png")
+plt.show()
+
+print(f"[DONE] Model saved to {MODEL_SAVE_PATH}")