Problem Statement: The core problem we are trying to solve is to help future dog owners find a dog who is a good fit for their lifestyle and family environment.

Solution: A user friendly app that helps connect future dog owners with dogs available for adoption.

Features:

• Help the user search for dogs based on certain features such as size and color.
• Find similar dogs by uploading a picture of a dog the user is interested
• Connect the dog with the user by allowing the user to chat with a persona of the dog.

Dog Search With Features

• Every candidate has a bunch of tags associated with her.
• When a user types in text in search box it is compared to available tags.
• When a user types in text in search box it is compared to available tags.
• Tags for the following picture can be:Retriever,Black

Dog Search With Images

• To get the embeddings we use EfficientNet.
• This model was finetuned on Stanford Dogs dataset.
• Workflow:

1. Generate embedding for query image.
2. Run a similarity search over existing embeddings using FAISS.
3. Return results in descending order of similarity.

Chatbot:

To build the chatbot we tried 3 different models:

1. BERT
2. GPT2
3. GPT2 DoubleHead

Chatbot

1. BERT

• Masked Language Model
• Made up of only the Encoder with stacked transformer blocks
• Bidirectional language model

2. GPT2

• Auto-regressive model (A word is predicted using words from its left context only)
• Made up of only the Decoder with stacked transformer blocks
• Unidirectional language model
• Good for generating text

3. GPT2 DoubleHead

• PERSONA-CHAT Dataset
• Dog Chat Dataset

DLOps