Add application file

.gitignore

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+# Python specific
+*.pyc
+__pycache__/
+
+# Virtual environment files (if using virtualenv or similar)
+venv/
+env/
+
+# Model checkpoints or large data files
+*.h5
+*.pkl
+
+# IDE files
+.vscode/
+.idea/
+
+# Gradio uploads folder
+uploads/
+

main.py

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+import os
+import numpy as np
+import pickle
+import tensorflow as tf
+from PIL import Image
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.layers import GlobalMaxPooling2D
+from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
+from sklearn.neighbors import NearestNeighbors
+from numpy.linalg import norm
+import gradio as gr
+
+# Function to load embeddings and filenames
+def load_data():
+    # Load embeddings and filenames from the same directory as main.py
+    embeddings_path = os.path.join(os.path.dirname(__file__), 'embeddings.pkl')
+    filenames_path = os.path.join(os.path.dirname(__file__), 'filenames.pkl')
+    
+    feature_list = np.array(pickle.load(open(embeddings_path, 'rb')))
+    filenames = pickle.load(open(filenames_path, 'rb'))
+    
+    return feature_list, filenames
+
+# Load the feature list and filenames
+feature_list, filenames = load_data()
+
+# Load the pre-trained model
+model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
+model.trainable = False
+model = tf.keras.Sequential([
+    model,
+    GlobalMaxPooling2D()
+])
+
+def feature_extraction(img_path, model):
+    img = image.load_img(img_path, target_size=(224, 224))
+    img_array = image.img_to_array(img)
+    expanded_img_array = np.expand_dims(img_array, axis=0)
+    preprocessed_img = preprocess_input(expanded_img_array)
+    result = model.predict(preprocessed_img).flatten()
+    normalized_result = result / norm(result)
+    return normalized_result
+
+def recommend(features, feature_list):
+    neighbors = NearestNeighbors(n_neighbors=6, algorithm='brute', metric='euclidean')
+    neighbors.fit(feature_list)
+    distances, indices = neighbors.kneighbors([features])
+    return indices
+
+def resize_image(img, size):
+    img = img.resize(size, Image.LANCZOS)
+    return img
+
+def predict(input_image):
+    img = input_image.convert("RGB")  # Ensure image is in RGB mode
+    img_array = np.array(img)
+    expanded_img_array = np.expand_dims(img_array, axis=0)
+    preprocessed_img = preprocess_input(expanded_img_array)
+    features = model.predict(preprocessed_img).flatten()
+    normalized_features = features / norm(features)
+    indices = recommend(normalized_features, feature_list)
+    result_images = [filenames[idx] for idx in indices[0]]
+    resized_images = [resize_image(Image.open(img_path), (224, 224)) for img_path in result_images]
+    return resized_images
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload an image"),
+    outputs=[gr.Image(type="pil", label=f"Recommendation {i+1}") for i in range(5)],
+    title="Amazon Lens",
+    description="Upload an image of clothing, and the system will recommend similar items."
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+gradio
+scikit-learn=1.2.2
+numpy=1.26.4
+Pillow=9.5.0
+tensorflow=2.15.0
+pickleshare=0.7.5