app.py

import streamlit as st
import numpy as np
import numpy.linalg as la
import pickle
import os
import gdown
from sentence_transformers import SentenceTransformer
import matplotlib.pyplot as plt
import math


# Compute Cosine Similarity
def cosine_similarity(x, y):
    """
    Exponentiated cosine similarity
    1. Compute cosine similarity
    2. Exponentiate cosine similarity
    3. Return exponentiated cosine similarity
    (20 pts)
    """
    ##################################
    ### TODO: Add code here ##########
    ##################################

    # Calculate the dot product of the vectors
    dot_product = np.dot(x, y)

    # Calculate the magnitude of the vectors
    mag_x = la.norm(x)
    mag_y = la.norm(y)

    # Calculate the cosine similarity
    if mag_x == 0 or mag_y == 0:
        cos_sim = 0
    else:
      cos_sim = dot_product / (mag_x * mag_y)

    # Exponentiate cosine similarity
    cos_sim = np.exp(cos_sim)

    return cos_sim
    

# Function to Load Glove Embeddings
def load_glove_embeddings(glove_path="Data/embeddings.pkl"):
    with open(glove_path, "rb") as f:
        embeddings_dict = pickle.load(f, encoding="latin1")

    return embeddings_dict


# Only 50d will be used per TA as the links don't work
def get_model_id_gdrive(model_type):
    if model_type == "25d":
        word_index_id = "13qMXs3-oB9C6kfSRMwbAtzda9xuAUtt8"
        embeddings_id = "1-RXcfBvWyE-Av3ZHLcyJVsps0RYRRr_2"
    elif model_type == "50d":
        embeddings_id = "1DBaVpJsitQ1qxtUvV1Kz7ThDc3az16kZ"
        word_index_id = "1rB4ksHyHZ9skes-fJHMa2Z8J1Qa7awQ9"
    elif model_type == "100d":
        word_index_id = "1-oWV0LqG3fmrozRZ7WB1jzeTJHRUI3mq"
        embeddings_id = "1SRHfX130_6Znz7zbdfqboKosz-PfNvNp"
        
    return word_index_id, embeddings_id


def download_glove_embeddings_gdrive(model_type):
    # Get glove embeddings from google drive
    word_index_id, embeddings_id = get_model_id_gdrive(model_type)

    # Use gdown to get files from google drive
    embeddings_temp = "embeddings_" + str(model_type) + "_temp.npy"
    word_index_temp = "word_index_dict_" + str(model_type) + "_temp.pkl"

    # Download word_index pickle file
    print("Downloading word index dictionary....\n")
    gdown.download(id=word_index_id, output=word_index_temp, quiet=False)

    # Download embeddings numpy file
    print("Donwloading embedings...\n\n")
    gdown.download(id=embeddings_id, output=embeddings_temp, quiet=False)


# @st.cache_data()
def load_glove_embeddings_gdrive(model_type):
    # Only 50d will be used per TA as the links don't work
    word_index_temp = "word_index_dict_" + str(model_type) + "_temp.pkl"
    embeddings_temp = "embeddings_" + str(model_type) + "_temp.npy"

    # Load word index dictionary
    word_index_dict = pickle.load(open(word_index_temp, "rb"), encoding="latin")

    # Load embeddings numpy
    embeddings = np.load(embeddings_temp)

    return word_index_dict, embeddings


@st.cache_resource()
def load_sentence_transformer_model(model_name):
    sentenceTransformer = SentenceTransformer(model_name)
    return sentenceTransformer


def get_sentence_transformer_embeddings(sentence, model_name="all-MiniLM-L6-v2"):
    """
    Get sentence transformer embeddings for a sentence
    """
    # 384 dimensional embedding
    # Default model: https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2  

    sentenceTransformer = load_sentence_transformer_model(model_name)

    try:
        return sentenceTransformer.encode(sentence)
    except:
        if model_name == "all-MiniLM-L6-v2":
            return np.zeros(384)
        else:
            return np.zeros(512)


def get_glove_embeddings(word, word_index_dict, embeddings, model_type):
    """
    Get glove embedding for a single word
    """
    if word.lower() in word_index_dict:
        return embeddings[word_index_dict[word.lower()]]
    else:
        return np.zeros(int(model_type.split("d")[0]))


def averaged_glove_embeddings_gdrive(sentence, word_index_dict, embeddings, model_type=50):
    """
    Get averaged glove embeddings for a sentence
    1. Split sentence into words
    2. Get embeddings for each word
    3. Add embeddings for each word
    4. Divide by number of words
    5. Return averaged embeddings
    (30 pts)
    """
    embedding = np.zeros(int(model_type.split("d")[0]))
    ##################################
    ##### TODO: Add code here ########
    ##################################

    # Split the sentence into words
    words = sentence.split()
    print(words)

    # Get embeddings for each word
    for word in words:
      # Add embeddings for each word
      embedding += get_glove_embeddings(word, word_index_dict, embeddings, model_type)

    # Divide by number of words
    if len(words) > 0:
      embedding /= len(words)

    return embedding


def get_category_embeddings(embeddings_metadata):
    """
    Get embeddings for each category
    1. Split categories into words
    2. Get embeddings for each word
    """
    model_name = embeddings_metadata["model_name"]
    st.session_state["cat_embed_" + model_name] = {}
    for category in input_categories.split(" "):
        # if model_name:
        #     if not category in st.session_state["cat_embed_" + model_name]:
        #         st.session_state["cat_embed_" + model_name][category] = get_sentence_transformer_embeddings(category, model_name=model_name)
        # else:
        if not category in st.session_state["cat_embed_" + model_name]:
            st.session_state["cat_embed_" + model_name][category] = get_sentence_transformer_embeddings(category)


def update_category_embeddings(embeddings_metadata):
    """
    Update embeddings for each category
    """
    get_category_embeddings(embeddings_metadata)


def get_sorted_cosine_similarity(embeddings_metadata):
    """
    Get sorted cosine similarity between input sentence and categories
    Steps:
    1. Get embeddings for input sentence
    2. Get embeddings for categories (if not found, update category embeddings)
    3. Compute cosine similarity between input sentence and categories
    4. Sort cosine similarity
    5. Return sorted cosine similarity
    (50 pts)
    """
    categories = input_categories.split(" ")
    cosine_sim = {}
    # Start here if it's GloVe
    if embeddings_metadata["embedding_model"] == "glove":
        word_index_dict = embeddings_metadata["word_index_dict"]
        embeddings = embeddings_metadata["embeddings"]
        model_name = embeddings_metadata["model_name"]

        input_embedding = averaged_glove_embeddings_gdrive(input_text_search,
                                                            word_index_dict,
                                                            embeddings, model_name)

        ##########################################
        ## TODO: Get embeddings for categories ###
        ##########################################
        # Make sure embeddings are updated using GloVe
        # Compute cosin similarity for each category using GloVe
        for category in categories:
            category_embedding = averaged_glove_embeddings_gdrive(category,
                                                                    word_index_dict,
                                                                    embeddings, model_name)
            similarity = cosine_similarity(input_embedding, category_embedding)
            cosine_sim[category] = similarity


    # Here for sentence
    else:
        model_name = embeddings_metadata["model_name"]
        # if not "cat_embed_" + model_name in st.session_state:
        get_category_embeddings(embeddings_metadata)
        # input_embedding = get_sentence_transformer_embeddings(input_text_search, model_name=model_name)


        # category_embeddings = st.session_state["cat_embed_" + model_name]

        print("text_search = ", input_text_search)
        if model_name:
            input_embedding = get_sentence_transformer_embeddings(input_text_search, model_name=model_name)
        else:
            input_embedding = get_sentence_transformer_embeddings(input_text_search)

        ##########################################
        # TODO: Compute cosine similarity between input sentence and categories
        # TODO: Update category embeddings if category not found
        ##########################################

        for category in categories:
            category_embedding = get_sentence_transformer_embeddings(category, model_name=model_name)
            similarity = cosine_similarity(input_embedding, category_embedding)
            cosine_sim[category] = similarity

    min_value = min(cosine_sim.values())
    max_value = max(cosine_sim.values())

    cosine_sim ={category: (value - min_value) / (max_value - min_value) if (max_value - min_value) != 0 else 0 for category, value in cosine_sim.items()}

    # Sort the cosine similarities
    sorted_cosine_sim = sorted(cosine_sim.items(), key=lambda x: x[1], reverse=True)
    print(sorted_cosine_sim)

    return sorted_cosine_sim


def plot_piechart(sorted_cosine_scores_items):
    sorted_cosine_scores = np.array([
            sorted_cosine_scores_items[index][1]
            for index in range(len(sorted_cosine_scores_items))
        ]
    )
    categories = input_categories.split(" ")
    categories_sorted = [
        categories[sorted_cosine_scores_items[index][0]]
        for index in range(len(sorted_cosine_scores_items))
    ]
    fig, ax = plt.subplots()
    ax.pie(sorted_cosine_scores, labels=categories_sorted, autopct="%1.1f%%")
    st.pyplot(fig)  # Figure


def plot_piechart_helper(sorted_cosine_scores_items):
    sorted_cosine_scores = np.array(
        [
            sorted_cosine_scores_items[index][1]
            for index in range(len(sorted_cosine_scores_items))
        ]
    )
    categories = input_categories.split(" ")
    categories_sorted = [item[0] for item in sorted_cosine_scores_items]
    # categories_sorted = [
    #     categories[sorted_cosine_scores_items[index][0]]
    #     for index in range(len(sorted_cosine_scores_items))
    # ]
    fig, ax = plt.subplots(figsize=(3, 3))
    my_explode = np.zeros(len(categories_sorted))
    my_explode[0] = 0.2
    if len(categories_sorted) == 3:
        my_explode[1] = 0.1  # explode this by 0.2
    elif len(categories_sorted) > 3:
        my_explode[2] = 0.05
    ax.pie(
        sorted_cosine_scores,
        labels=categories_sorted,
        autopct="%1.1f%%",
        explode=my_explode,
    )

    return fig


def plot_piecharts(sorted_cosine_scores_models):
    scores_list = []
    categories = input_categories.split(" ")
    index = 0
    for model in sorted_cosine_scores_models:
        scores_list.append(sorted_cosine_scores_models[model])
        # scores_list[index] = np.array([scores_list[index][ind2][1] for ind2 in range(len(scores_list[index]))])
        index += 1

    if len(sorted_cosine_scores_models) == 2:
        fig, (ax1, ax2) = plt.subplots(2)

        categories_sorted = [
            categories[scores_list[0][index][0]] for index in range(len(scores_list[0]))
        ]
        sorted_scores = np.array(
            [scores_list[0][index][1] for index in range(len(scores_list[0]))]
        )
        ax1.pie(sorted_scores, labels=categories_sorted, autopct="%1.1f%%")

        categories_sorted = [
            categories[scores_list[1][index][0]] for index in range(len(scores_list[1]))
        ]
        sorted_scores = np.array(
            [scores_list[1][index][1] for index in range(len(scores_list[1]))]
        )
        ax2.pie(sorted_scores, labels=categories_sorted, autopct="%1.1f%%")

    st.pyplot(fig)


def plot_alatirchart(sorted_cosine_scores_models):
    models = list(sorted_cosine_scores_models.keys())
    tabs = st.tabs(models)
    figs = {}
    for model in models:
        figs[model] = plot_piechart_helper(sorted_cosine_scores_models[model])

    for index in range(len(tabs)):
        with tabs[index]:
            st.pyplot(figs[models[index]])


### Text Search ###
st.sidebar.title("GloVe Twitter")
st.sidebar.markdown(
    """
GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Pretrained on 
2 billion tweets with vocabulary size of 1.2 million. Download from [Stanford NLP](http://nlp.stanford.edu/data/glove.twitter.27B.zip). 

Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. *GloVe: Global Vectors for Word Representation*.
"""
)

# ONLY MODEL 50d WILL BE USED PER TA
model_type = st.sidebar.selectbox("Choose the model", ("50d", "50d"), index=1)

# Initialize Session State
# Errors because the session state is not updated until the app is rerun
# if 'categories' not in st.session_state:
#     st.session_state['categories'] = 'Flowers Colors Cars Weather Food'


st.title("Search Based Retrieval Demo")
st.subheader(
    "Pass in space separated categories you want this search demo to be about."
)
# st.selectbox(label="Pick the categories you want this search demo to be about...",
# options=("Flowers Colors Cars Weather Food", "Chocolate Milk", "Anger Joy Sad Frustration Worry Happiness", "Positive Negative"),
# key="categories"
# )
input_categories = st.text_input(
    label="Categories", key="categories", value="Flowers Colors Cars Weather Food"
)
if 'categories' in st.session_state:
    print(st.session_state["categories"])
    print(type(st.session_state["categories"]))

st.subheader("Pass in an input word or even a sentence")
text_search = st.text_input(
    label="Input your sentence",
    key="text_search",
    value="Roses are red, trucks are blue, and Seattle is grey right now",
)
input_text_search = text_search

# Download glove embeddings if it doesn't exist
embeddings_path = "embeddings_" + str(model_type) + "_temp.npy"
word_index_dict_path = "word_index_dict_" + str(model_type) + "_temp.pkl"
if not os.path.isfile(embeddings_path) or not os.path.isfile(word_index_dict_path):
    print("Model type = ", model_type)
    glove_path = "Data/glove_" + str(model_type) + ".pkl"
    print("glove_path = ", glove_path)

    # Download embeddings from google drive
    # Disabled due to unable to access drive files from access control
    # Files are stored locally and only the 50d is needed
    # with st.spinner("Downloading glove embeddings..."):
    #     download_glove_embeddings_gdrive(model_type)


# Load glove embeddings
word_index_dict, embeddings = load_glove_embeddings_gdrive(model_type)


# Find closest word to an input word
if input_text_search:
    # Glove embeddings
    print("Glove Embedding")
    embeddings_metadata = {
        "embedding_model": "glove",
        "word_index_dict": word_index_dict,
        "embeddings": embeddings,
        "model_name": model_type,
    }
    with st.spinner("Obtaining Cosine similarity for Glove..."):
        sorted_cosine_sim_glove = get_sorted_cosine_similarity(
            embeddings_metadata
        )

    # Sentence transformer embeddings
    print("Sentence Transformer Embedding")
    embeddings_metadata = {"embedding_model": "transformers", "model_name": ""}
    with st.spinner("Obtaining Cosine similarity for 384d sentence transformer..."):
        sorted_cosine_sim_transformer = get_sorted_cosine_similarity(
            embeddings_metadata
        )

    # Results and Plot Pie Chart for Glove
    print("Categories are: ", input_categories)
    st.subheader(
        "Closest word I have between: "
        + input_categories
        + " as per different Embeddings"
    )

    print(sorted_cosine_sim_glove)
    print(sorted_cosine_sim_transformer)
    # print(sorted_distilbert)
    # Altair Chart for all models
    plot_alatirchart(
        {
            "glove_" + str(model_type): sorted_cosine_sim_glove,
            "sentence_transformer_384": sorted_cosine_sim_transformer,
        }
    )
    # "distilbert_512": sorted_distilbert})

    st.write("")
    st.write(
        "Demo developed by [Dr. Karthik Mohan](https://www.linkedin.com/in/karthik-mohan-72a4b323/)"
    )