save changes

README.md

@@ -1,13 +1,14 @@
 ---
-title: Testapp Cats Dogs Cnn Mode Tf2
-emoji: 🏢
-colorFrom: blue
-colorTo: purple
+title: Testapp Cats Dogs Cnn Mode Tf
+emoji: 🧠
+colorFrom: green
+colorTo: yellow
 sdk: streamlit
-sdk_version: 1.42.0
+sdk_version: 1.41.1
 app_file: app.py
 pinned: false
 license: mit
+short_description: App to test the CNN Model pf the cats and dogs recognition
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,101 @@
+import streamlit as st
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import os
+from huggingface_hub import login
+from tensorflow import keras
+import requests
+
+# Get Hugging Face token from environment variables
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
+if not HF_TOKEN:
+    st.error("HF_TOKEN environment variable not set. Please set it before running the app.")
+    st.stop()  # Stop execution if token is missing
+
+# Authenticate with Hugging Face
+try:
+    login(token=HF_TOKEN)
+    st.success("Successfully logged in to Hugging Face Hub!")
+except Exception as e:
+    st.error(f"Hugging Face login failed: {e}")
+    st.stop()
+
+model_url = "https://huggingface.co/louiecerv/cats_dogs_recognition_tf_cnn/resolve/main/cats_dogs_recognition_tf_cnn.keras"
+model_filename = "cats_dogs_recognition_tf_cnn.keras"  # Or any name you prefer
+model_filepath = model_filename  # Save in the current directory
+
+try:
+    # 1. Download the model file
+    response = requests.get(model_url, stream=True)
+    response.raise_for_status()  # Raise an exception for bad status codes (4xx or 5xx)
+    with open(model_filepath, "wb") as f:
+        for chunk in response.iter_content(chunk_size=8192):
+            f.write(chunk)
+
+    # 2. Load the model from the local file
+    model = tf.keras.models.load_model(model_filepath)
+    st.success(f"Model loaded successfully from: {model_filepath}")
+
+    # Optional: Clean up the downloaded file (if desired)
+    # os.remove(model_filepath)  # Uncomment if you want to delete the file after loading
+
+except requests.exceptions.RequestException as e:
+    st.error(f"Error downloading model: {e}")
+    st.stop()
+except Exception as e:
+    st.error(f"Error loading model: {e}")
+    st.stop()
+
+# Image preprocessing function with cropping
+def preprocess_image(image):
+    # 1. Crop to square aspect ratio
+    width, height = image.size
+    new_size = min(width, height)
+    left = (width - new_size) // 2
+    top = (height - new_size) // 2
+    right = (width + new_size) // 2
+    bottom = (height + new_size) // 2
+    image = image.crop((left, top, right, bottom))
+
+    # 2. Resize
+    image = image.resize((128, 128))  # Use PIL's resize for consistency
+
+    # 3. Convert to NumPy array if it's a PIL image
+    image_np = np.array(image)
+
+    # 4. Ensure 3 channels (RGB)
+    if image_np.ndim == 2 or image_np.shape[2] == 1:
+        image_np = np.stack([image_np] * 3, axis=-1)
+
+    # 5. Normalize
+    image_np = image_np.astype(np.float32) / 255.0
+    image_np = np.expand_dims(image_np, axis=0)
+    return image_np
+
+# Streamlit app
+st.title("Cat vs. Dog Image Classifier")
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image.", use_container_width=True)
+
+    processed_image = preprocess_image(image)  # Pass the PIL Image directly
+
+    # Display preprocessed image (optional, but helpful for debugging)
+    processed_image_display = tf.squeeze(processed_image, axis=0).numpy() * 255.0
+    processed_image_display = processed_image_display.astype(np.uint8)
+    processed_image_display = Image.fromarray(processed_image_display)
+    st.image(processed_image_display, caption="Preprocessed Image", use_container_width=True)
+
+
+    predictions = model.predict(processed_image)
+    class_index = np.argmax(predictions)
+    class_names = ["Cat", "Dog"]
+    predicted_class = class_names[class_index]
+    confidence = predictions[0][class_index] * 100
+
+    st.write(f"## Prediction: {predicted_class} (Confidence: {confidence:.2f}%)")

requirements.txt

@@ -0,0 +1,7 @@
+streamlit
+tensorflow
+Pillow
+numpy
+transformers
+torch
+keras