Add 'About' section to explain app purpose and features

app.py

@@ -7,17 +7,32 @@ import altair as alt
 from PIL import Image
 from tensorflow.keras.preprocessing.image import ImageDataGenerator
 import zipfile
+import io
+import shutil
 
 # 🎨 App Title
 st.title("🐶🐱 Cat vs Dog Classifier")
 
+# 📖 About the App
+st.write(
+    """
+    ## About This App
+    This is a machine learning application that classifies images into two categories:
+    **Cats 🐱** and **Dogs 🐶**. The model is trained using a deep learning architecture
+    called Convolutional Neural Networks (CNNs) and is able to distinguish between images 
+    of cats and dogs with high accuracy.
+    ### Features:
+    - **Dataset Overview**: View the number of images in the dataset, categorized by "Cats" and "Dogs".
+    - **Model Evaluation**: Check the model's performance on the validation set, including accuracy and loss.
+    - **Image Classification**: Upload an image, and the model will predict whether it's a cat or a dog, along with the confidence level.
+    - **Download Test Folder**: Download a ZIP file containing the test images.
+    The app is powered by **Streamlit** for an interactive user interface and **TensorFlow** for image classification.
+    """
+)
+
 # ✅ Detect Environment & Set Dataset Path
-if os.getenv("HF_HOME"):  # Hugging Face
-    BASE_DIR = "/home/user/app/dataset"
-    ZIP_PATH = "/home/user/app/dataset.zip"  # If dataset is uploaded as a ZIP
-else:  # Local Machine
-    BASE_DIR = r"C:\Users\Cherilyn\Downloads\CCS229\cat_vs._dog_classifier\dataset"
-    ZIP_PATH = None  # No ZIP needed locally
+BASE_DIR = "dataset"  # In Hugging Face Spaces, the dataset folder should be at the root of the Space
+ZIP_PATH = "dataset.zip"  # If dataset is uploaded as a ZIP (make sure it's in the same directory as app.py)
 
 TRAIN_DIR = os.path.join(BASE_DIR, "train")
 TEST_DIR = os.path.join(BASE_DIR, "test")
@@ -26,8 +41,8 @@ TEST_DIR = os.path.join(BASE_DIR, "test")
 if ZIP_PATH and os.path.exists(ZIP_PATH):
     if not os.path.exists(BASE_DIR):  # Avoid re-extracting
         with zipfile.ZipFile(ZIP_PATH, "r") as zip_ref:
-            zip_ref.extractall("/home/user/app/")
-    print("✅ Dataset extracted!")
+            zip_ref.extractall(BASE_DIR)  # Extract into dataset folder
+    st.success("✅ Dataset extracted!")
 
 # 📌 Check if dataset exists
 if not os.path.exists(TRAIN_DIR):
@@ -45,13 +60,13 @@ if not os.path.exists(cat_dir) or not os.path.exists(dog_dir):
 # 📌 Constants
 IMG_SIZE = (150, 150)
 BATCH_SIZE = 32
-MODEL_PATH = "cats_dogs_model.h5"  # Fixed model filename
+MODEL_PATH = "cats_dogs_model.h5"  # Ensure the model is uploaded to Hugging Face Space
 
 # 🎯 Load Model
 if os.path.exists(MODEL_PATH):
     model = tf.keras.models.load_model(MODEL_PATH)
 else:
-    st.error("⚠ No trained model found. Please upload 'cat_dog_model.h5' to your Hugging Face repository.")
+    st.error("⚠ No trained model found. Please upload 'cats_dogs_model.h5' to your Hugging Face repository.")
     st.stop()
 
 # 📷 Image Preprocessing
@@ -131,3 +146,27 @@ with tab3:
 
         st.subheader("Prediction:")
         st.write(f"This is a **{label}** with **{confidence*100:.2f}%** confidence.")
+
+    # **New Feature: Download the 'test' folder as a ZIP**
+    def zip_folder(folder_path):
+        # Create an in-memory zip file
+        zip_buffer = io.BytesIO()
+        with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zip_file:
+            for root, dirs, files in os.walk(folder_path):
+                for file in files:
+                    zip_file.write(os.path.join(root, file), os.path.relpath(os.path.join(root, file), folder_path))
+        zip_buffer.seek(0)  # Go to the beginning of the file
+        return zip_buffer
+
+    # Button to download 'test' folder
+    if os.path.exists(TEST_DIR):
+        st.write("### Download Test Folder")
+        zip_buffer = zip_folder(TEST_DIR)
+        st.download_button(
+            label="Download Test Folder as ZIP",
+            data=zip_buffer,
+            file_name="test_folder.zip",
+            mime="application/zip"
+        )
+    else:
+        st.warning(f"❌ Test folder not found at `{TEST_DIR}`")

dataset.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e740e196ea75b73fed88e3b7c7c45b2d15714060814c02e90c7151be68e8d1d7
+size 67592295