main

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+.pytest_cache/
+.hypothesis/
+
+# Environments
+.env
+.venv
+venv/
+ENV/
+env/
+venv.bak/
+.vscode/
+
+# Streamlit secrets
+.streamlit/
+
+# Local model files
+*.keras
+*.h5

app.py

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+
+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from PIL import Image
+import numpy as np
+import requests
+import os
+from tqdm import tqdm
+
+# Set page config
+st.set_page_config(
+    page_title="Cat vs. Dog Classifier",
+    page_icon="🐾",
+    layout="wide",
+    initial_sidebar_state="expanded",
+)
+
+# Custom CSS for styling
+st.markdown(
+    """
+    <style>
+    .main {
+        background-color: #f5f5f5;
+    }
+    .st-bk {
+        background-color: #ffffff;
+        border-radius: 10px;
+        padding: 20px;
+    }
+    .st-emotion-cache-1v0mbdj > img{
+        border-radius: 10px;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+# --- Model Downloading and Loading ---
+def download_file_from_google_drive(id, destination):
+    URL = f'https://drive.google.com/uc?export=download&id={id}'
+    session = requests.Session()
+    response = session.get(URL, stream=True)
+    
+    token = None
+    for key, value in response.cookies.items():
+        if key.startswith('download_warning'):
+            token = value
+            break
+    
+    if token:
+        params = {'id': id, 'confirm': token}
+        response = session.get(URL, params=params, stream=True)
+    
+    total_size = int(response.headers.get('content-length', 0))
+    block_size = 1024  # 1 Kibibyte
+    
+    progress_bar = tqdm(total=total_size, unit='iB', unit_scale=True)
+    with open(destination, 'wb') as f:
+        for data in response.iter_content(block_size):
+            progress_bar.update(len(data))
+            f.write(data)
+    progress_bar.close()
+    
+    if total_size != 0 and progress_bar.n != total_size:
+        st.error("An error occurred during file download.")
+        return False
+    return True
+
+@st.cache_resource
+def load_keras_model():
+    """
+    Downloads the model from Google Drive if not present, then loads it.
+    The `st.cache_resource` decorator ensures the model is loaded only once.
+    """
+    MODEL_PATH = "my_model.keras"
+    FILE_ID = "1M-HNEJqbz6PzjhX6WHHKLPbjZpPRWLjP" # Replace with your file ID
+
+    if not os.path.exists(MODEL_PATH):
+        st.info("Model not found locally. Downloading from Google Drive... (this may take a moment)")
+        download_file_from_google_drive(FILE_ID, MODEL_PATH)
+        st.success("Model downloaded successfully!")
+
+    try:
+        model = load_model(MODEL_PATH)
+        return model
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+        st.info("Please ensure the Google Drive File ID is correct and the file is accessible.")
+        return None
+
+model = load_keras_model()
+
+# --- Image Preprocessing ---
+def preprocess_image(image):
+    """
+    Preprocesses the uploaded image to fit the model's input requirements.
+    - Resizes to (256, 256)
+    - Converts to a NumPy array
+    - Normalizes pixel values
+    - Expands dimensions for the model
+    """
+    img = image.resize((256, 256))
+    img_array = np.array(img)
+    img_array = img_array / 255.0
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    return img_array
+
+# --- UI Layout ---
+st.title("🐾 Cat vs. Dog Image Classifier")
+st.markdown(
+    "Upload an image of a cat or a dog, and the model will predict which one it is!"
+)
+
+col1, col2 = st.columns(2)
+
+with col1:
+    st.header("Upload Image")
+    uploaded_file = st.file_uploader(
+        "Choose an image...", type=["jpg", "jpeg", "png"]
+    )
+
+if uploaded_file is not None and model is not None:
+    # Display the uploaded image
+    image = Image.open(uploaded_file)
+    
+    with col1:
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Preprocess the image and make a prediction
+    processed_image = preprocess_image(image)
+    prediction = model.predict(processed_image)
+    confidence = prediction[0][0]
+
+    with col2:
+        st.header("Prediction")
+        if confidence > 0.5:
+            st.markdown(
+                f"## This is a Dog! 🐶"
+            )
+            st.progress(confidence)
+            st.write(f"**Confidence:** {confidence:.2f}")
+        else:
+            st.markdown(
+                f"## This is a Cat! 🐱"
+            )
+            st.progress(1-confidence)
+            st.write(f"**Confidence:** {1-confidence:.2f}")
+else:
+    with col2:
+        st.info("Please upload an image to see the prediction.")
+

requirements.txt

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+streamlit
+tensorflow
+numpy
+Pillow
+requests
+tqdm