---
title: Goody Goody Fashion Store
emoji: 🏆
colorFrom: yellow
colorTo: indigo
sdk: streamlit
sdk_version: 1.26.0
app_file: app.py
pinned: false
license: apache-2.0
---

"FASHION RECOMMENDER SYSTEM"

Game Plan Image Dataset: Start with a dataset containing 44,000 fashion images.

Pre-trained Model - ResNet-50: Utilize the pre-trained ResNet-50 model, a convolutional neural network (CNN) trained on a large dataset for image classification tasks.

Extracting Embeddings: Pass each image through the ResNet-50 model to extract feature embeddings. The output will be a numerical vector (embedding) that represents the image's features

Embedding Extraction Function: Define a function to extract embeddings using the pre-trained ResNet-50 model. This function takes an image as input and returns the corresponding feature embedding.

Search for Similar Embeddings: When a new image is presented to the system: Use the embedding extraction function to obtain the embedding for the new image. Compare this embedding with the embeddings of all images in the dataset.

Similarity Ranking: Calculate the similarity between the embeddings (e.g., cosine similarity). Rank the images based on their similarity to the embedding of the new image.

Top 5 Similar Images: Select the top 5 images with the highest similarity scores.

Display Recommendations: Display the top 5 images as fashion recommendations for the given new image.


Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference