Initial commit of my Streamlit app

.streamlit/config.toml

@@ -0,0 +1,2 @@
+[theme]
+base = "light"

README.md

@@ -12,3 +12,42 @@ short_description: An AI model that predicts the breed of a dog from an image
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+
+# 🐶 The DogID App
+
+Welcome to **The DogID App** — a fast and fun way to identify dog breeds using deep learning. Just upload a dog image and our app will predict the most likely breed out of 120 possibilities.
+
+## 🚀 How it works
+
+This app uses a fine-tuned MobileNetV2 deep learning model trained on the [Stanford Dogs Dataset](http://vision.stanford.edu/aditya86/ImageNetDogs/) and powered by TensorFlow and Streamlit.
+
+## 📦 Features
+
+- 📸 Upload your own dog photo
+- 🧠 Real-time breed predictions
+- 🌐 Deployed on Hugging Face Spaces
+- 🎨 Light mode enforced for consistent visuals
+
+## 🛠️ Built With
+
+- Python 🐍
+- TensorFlow / Keras
+- Streamlit
+- Hugging Face Spaces
+
+## 📂 File Overview
+
+- `app.py` – Streamlit app script
+- `requirements.txt` – Python dependencies
+- `.streamlit/config.toml` – Forces light mode
+- `dog_breed_ID_batch32_cache_prefetch.keras` – Trained model
+- `assets/` – Logo and image files
+
+## 📝 License
+
+This project is licensed under the MIT License — see `LICENSE` file for details.
+
+---
+
+Enjoy using **The DogID App**! 🐾

app.py

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+import streamlit as st
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+import tensorflow_hub as hub
+import tempfile
+import urllib.request
+
+# APP user interface configuration
+st.set_page_config(page_title="Dog Breed Identifier", layout="centered")
+
+# Add my logo
+col1, col2, col3 = st.columns([1, 2, 1])
+
+with col2:
+    st.image("assets/MLOwl_ca_logo_no_bkg_black_cropped.png", width=300)
+
+# Load the model
+
+
+@st.cache_resource
+def load_model():
+    url = "https://huggingface.co/turtlemb/dogID_app_model/resolve/main/dog_breed_ID_batch32_cache_prefetch.keras"
+    with tempfile.NamedTemporaryFile(suffix=".keras") as tmp:
+        urllib.request.urlretrieve(url, tmp.name)
+        model = tf.keras.models.load_model(tmp.name, custom_objects={"KerasLayer": hub.KerasLayer})
+    return model
+
+model = load_model()
+
+# Define the class names (120 breeds)
+class_names = np.array([
+    'affenpinscher', 'afghan_hound', 'african_hunting_dog', 'airedale',
+    'american_staffordshire_terrier', 'appenzeller', 'australian_terrier',
+    'basenji', 'basset', 'beagle', 'bedlington_terrier', 'bernese_mountain_dog',
+    'black-and-tan_coonhound', 'blenheim_spaniel', 'bloodhound', 'bluetick',
+    'border_collie', 'border_terrier', 'borzoi', 'boston_bull',
+    'bouvier_des_flandres', 'boxer', 'brabancon_griffon', 'briard',
+    'brittany_spaniel', 'bull_mastiff', 'cairn', 'cardigan',
+    'chesapeake_bay_retriever', 'chihuahua', 'chow', 'clumber',
+    'cocker_spaniel', 'collie', 'curly-coated_retriever', 'dandie_dinmont',
+    'dhole', 'dingo', 'doberman', 'english_foxhound', 'english_setter',
+    'english_springer', 'entlebucher', 'eskimo_dog', 'flat-coated_retriever',
+    'french_bulldog', 'german_shepherd', 'german_short-haired_pointer',
+    'giant_schnauzer', 'golden_retriever', 'gordon_setter', 'great_dane',
+    'great_pyrenees', 'greater_swiss_mountain_dog', 'groenendael',
+    'ibizan_hound', 'irish_setter', 'irish_terrier', 'irish_water_spaniel',
+    'irish_wolfhound', 'italian_greyhound', 'japanese_spaniel', 'keeshond',
+    'kelpie', 'kerry_blue_terrier', 'komondor', 'kuvasz',
+    'labrador_retriever', 'lakeland_terrier', 'leonberg', 'lhasa',
+    'malamute', 'malinois', 'maltese_dog', 'mexican_hairless',
+    'miniature_pinscher', 'miniature_poodle', 'miniature_schnauzer',
+    'newfoundland', 'norfolk_terrier', 'norwegian_elkhound',
+    'norwich_terrier', 'old_english_sheepdog', 'otterhound', 'papillon',
+    'pekinese', 'pembroke', 'pomeranian', 'pug', 'redbone',
+    'rhodesian_ridgeback', 'rottweiler', 'saint_bernard', 'saluki',
+    'samoyed', 'schipperke', 'scotch_terrier', 'scottish_deerhound',
+    'sealyham_terrier', 'shetland_sheepdog', 'shih-tzu', 'siberian_husky',
+    'silky_terrier', 'soft-coated_wheaten_terrier',
+    'staffordshire_bullterrier', 'standard_poodle', 'standard_schnauzer',
+    'sussex_spaniel', 'tibetan_mastiff', 'tibetan_terrier', 'toy_poodle',
+    'toy_terrier', 'vizsla', 'walker_hound', 'weimaraner',
+    'welsh_springer_spaniel', 'west_highland_white_terrier', 'whippet',
+    'wire-haired_fox_terrier', 'yorkshire_terrier'
+])
+
+# Preprocessing the image
+
+
+def preprocess(image: Image.Image):
+    image = image.resize((224, 224))
+    array = np.array(image) / 255.0
+    return np.expand_dims(array, axis=0)
+
+
+# App user interface
+with st.container():
+    st.title("🐕 The DogID App")
+    st.markdown("<p style = 'text-align: left; color: gray;'> by Martin Bijloos | MLOwl.ca</p>",
+                unsafe_allow_html=True)
+    st.write("Upload a photo of a dog and get the breed prediction!")
+
+    uploaded_file = st.file_uploader(
+        "Upload a dog image", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file:
+        image = Image.open(uploaded_file).convert("RGB")
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+
+        if st.button("Classify"):
+            st.info("Processing...")
+            input_tensor = preprocess(image)
+            prediction = model.predict(input_tensor)[0]
+
+            top_k = 3
+            top_indices = prediction.argsort()[-top_k:][::-1]
+            top_classes = class_names[top_indices]
+            top_confidences = prediction[top_indices]
+
+            st.success("Top Predictions:")
+            for breed, score in zip(top_classes, top_confidences):
+                st.write(
+                    f"- **{breed.replace('_', ' ').title()}**: {score:.2%}")

requirements.txt

@@ -0,0 +1,69 @@
+absl-py==2.3.1
+altair==5.5.0
+astunparse==1.6.3
+attrs==25.3.0
+blinker==1.9.0
+cachetools==5.5.2
+certifi==2025.7.9
+charset-normalizer==3.4.2
+click==8.2.1
+flatbuffers==25.2.10
+gast==0.6.0
+gitdb==4.0.12
+GitPython==3.1.44
+google-auth==2.40.3
+google-auth-oauthlib==1.2.2
+google-pasta==0.2.0
+grpcio==1.73.1
+h5py==3.14.0
+idna==3.10
+Jinja2==3.1.6
+jsonschema==4.24.0
+jsonschema-specifications==2025.4.1
+keras==2.15.0
+libclang==18.1.1
+Markdown==3.8.2
+markdown-it-py==3.0.0
+MarkupSafe==3.0.2
+mdurl==0.1.2
+ml-dtypes==0.2.0
+narwhals==1.46.0
+numpy==1.26.4
+oauthlib==3.3.1
+opt_einsum==3.4.0
+packaging==24.2
+pandas==2.3.1
+pillow==10.4.0
+protobuf==4.25.8
+pyarrow==20.0.0
+pyasn1==0.6.1
+pyasn1_modules==0.4.2
+pydeck==0.9.1
+Pygments==2.19.2
+python-dateutil==2.9.0.post0
+pytz==2025.2
+referencing==0.36.2
+requests==2.32.4
+requests-oauthlib==2.0.0
+rich==13.9.4
+rpds-py==0.26.0
+rsa==4.9.1
+six==1.17.0
+smmap==5.0.2
+streamlit==1.35.0
+tenacity==8.5.0
+tensorboard==2.15.2
+tensorboard-data-server==0.7.2
+tensorflow==2.15.0
+tensorflow-estimator==2.15.0
+tensorflow-hub==0.15.0
+tensorflow-io-gcs-filesystem==0.37.1
+tensorflow-macos==2.15.0
+termcolor==3.1.0
+toml==0.10.2
+tornado==6.5.1
+typing_extensions==4.14.1
+tzdata==2025.2
+urllib3==2.5.0
+Werkzeug==3.1.3
+wrapt==1.14.1