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.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+heels.jpg filter=lfs diff=lfs merge=lfs -text
+shirt.jpg filter=lfs diff=lfs merge=lfs -text

app.py

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+import requests
+from PIL import Image
+import numpy as np
+import pandas as pd
+import io
+import pickle
+from tensorflow.keras.applications import VGG16
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.vgg16 import preprocess_input
+from tensorflow.keras.models import Model
+import gradio as gr
+from sklearn.metrics.pairwise import cosine_similarity
+
+# Load precomputed data
+with open('image_names.pkl', 'rb') as file:
+    image_names = pickle.load(file)
+
+with open('features_matrix.pkl', 'rb') as file:
+    features_matrix = pickle.load(file)
+
+image_df = pd.read_csv('images.csv')
+
+# VGG16
+base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
+model = Model(inputs=base_model.input, outputs=base_model.output)
+
+def extract_feature_from_image(img, model):
+    """Extract features from a single image using the model."""
+    img = preprocess_image(img)
+    features = model.predict(img)
+    return features.reshape(-1)  # Flatten the feature vector
+
+def preprocess_image(img, target_size=(224, 224)):
+    """Preprocess a single image for feature extraction."""
+    img = img.resize(target_size)
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = preprocess_input(img_array)
+    return img_array
+
+def find_similar_images(custom_image_features, features_matrix, top_n=5):
+    """Find the most similar images to the custom image."""
+    similarities = cosine_similarity([custom_image_features], features_matrix)
+    most_similar_indices = np.argsort(similarities[0])[::-1][:top_n]
+    return most_similar_indices
+
+def recommend_similar_images(img):
+    """Recommend similar images based on the uploaded image."""
+    query_feature = extract_feature_from_image(img, model)
+    similar_indices = find_similar_images(query_feature, features_matrix, top_n=5)
+    similar_images = [image_df['link'][image_df['filename'] == image_names[idx]].values[0] for idx in similar_indices]
+    return similar_images
+
+def get_recommendations_from_image(uploaded_image):
+    """Recommend similar images based on the uploaded image."""
+    img = uploaded_image
+    similar_image_urls = recommend_similar_images(img)
+    similar_images = [Image.open(requests.get(img_url, stream=True).raw) for img_url in similar_image_urls]
+    return similar_images
+
+# Gradio interface
+image_input = gr.Image(type="pil", label="Upload Image")
+outputs = gr.Gallery(label="Recommended Images", columns=3)  # Set the number of columns to fit images better
+
+gr.Interface(fn=get_recommendations_from_image, inputs=image_input, outputs=outputs, examples=['shirt.jpg','perfume.jpg','shoes.jpg','kurti.jpg','watches.jpg','heels.jpg','necklace.jpg'],
+             title="Fashion Image Recommender").launch()

features_matrix.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:655b407275b04f831035488161a9ad0b648626ea68933bd0d7063bbbca42708a
+size 3449800867

image_names.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:42eb7d6e242d7573d59510126c809b8747d25695c442dd8f9e2b9383c4d81be6
+size 407150