## Mini Project 1 - Part 1: Getting Familiar with Word Embeddings.
# This assignment introduces students to text similarity measures using cosine similarity and sentence embeddings. 
# Students will implement and compare different methods for computing and analyzing text similarity using GloVe and Sentence Transformers.

#Learning Objectives
#By the end of this assignment, students will:
#Understand how cosine similarity is used to measure text similarity.
#Learn to encode sentences using GloVe embeddings and Sentence Transformers.
#Compare the performance of different embedding techniques.
#Create a Web interface for your model

# Context: In this part, you are going to play around with some commonly used pretrained text embeddings for text search. For example, 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*.


### Import necessary libraries: here you will use streamlit library to run a text search demo, please make sure to install it.
import streamlit as st
import numpy as np
import pickle
import os
import gdown
from sentence_transformers import SentenceTransformer
import matplotlib.pyplot as plt


### Some predefined utility functions for you to load the text embeddings

# 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  #  A dictionary where the keys are words (or tokens) and the values are their corresponding GloVe embeddings.


def get_model_id_gdrive(model_type):
    if model_type == "25d":  # the dimension of the GloVe embeddings
        word_index_id = "13qMXs3-oB9C6kfSRMwbAtzda9xuAUtt8"  # Google Drive ID for the word index dictionary
        embeddings_id = "1-RXcfBvWyE-Av3ZHLcyJVsps0RYRRr_2"  # Google Drive ID for the embeddings file.
    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):
    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 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 st.session_state.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)


### Plotting utility functions

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 = st.session_state.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 = st.session_state.categories.split(" ")
    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 = st.session_state.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]])


### Your Part To Complete: Follow the instructions in each function below to complete the similarity calculation between text embeddings

# Task I: 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 ##########
    ##################################

    # Ensure inputs are NumPy arrays
    x = np.array(x)
    y = np.array(y)

    # Compute dot product
    dot_product = np.dot(x, y)

    # Compute L2 norms of both vectors
    norm_x = np.linalg.norm(x)
    norm_y = np.linalg.norm(y)

    # Compute cosine similarity
    cosine_sim = dot_product / (norm_x * norm_y)

    # Exponentiate cosine similarity
    exp_cosine_sim = np.exp(cosine_sim)

    return exp_cosine_sim


# Task II: Average Glove Embedding Calculation
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 sentence into words and convert to lowercase
    words = sentence.lower().split()

    # track the number of valid words found in the embeddings
    valid_word_count = 0

    for word in words:
        if word in word_index_dict:  # Check if the word exists in the vocabulary
            index = word_index_dict[word]  # Get the word's index in embeddings
            embedding += embeddings[index]  # Sum the corresponding embedding vector
            valid_word_count += 1

    # Compute the average embedding if any valid words were found
    if valid_word_count > 0:
        embedding /= valid_word_count

    return embedding


# Task III: Sort the cosine similarity
# def get_sorted_cosine_similarity(embeddings_metadata):
# def get_sorted_cosine_similarity(embeddings_metadata, categories_input=None):
def get_sorted_cosine_similarity(text_search, 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 = st.session_state.categories.split(" ")
    # categories = categories_input if categories_input is not None else st.session_state.categories.split(" ")
    cosine_sim = {}
    if embeddings_metadata["embedding_model"] == "glove":
        word_index_dict = embeddings_metadata["word_index_dict"]
        embeddings = embeddings_metadata["embeddings"]
        model_type = embeddings_metadata["model_type"]

        input_embedding = averaged_glove_embeddings_gdrive(text_search,
                                                           word_index_dict,
                                                           embeddings, model_type)

        ##########################################
        ## TODO: Get embeddings for categories ###
        ##########################################
        for index, category in enumerate(categories):
            category_embedding = averaged_glove_embeddings_gdrive(
                category,
                word_index_dict,
                embeddings,
                model_type)
            cosine_sim[index] = cosine_similarity(input_embedding, category_embedding)

    else:
        model_name = embeddings_metadata["model_name"]
        if not "cat_embed_" + model_name in st.session_state:
            get_category_embeddings(embeddings_metadata)

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

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

        for index in range(len(categories)):
            ##########################################
            # TODO: Compute cosine similarity between input sentence and categories
            # TODO: Update category embeddings if category not found  
            ##########################################
            category = categories[index]
            if category in category_embeddings:
                category_embedding = category_embeddings[category]
                cosine_sim[index] = cosine_similarity(input_embedding, category_embedding)
            else:
                update_category_embeddings(embeddings_metadata)
                category_embedding = st.session_state["cat_embed_" + model_name][category]
                cosine_sim[index] = cosine_similarity(input_embedding, category_embedding)

    # Sort cosine similarities in descending order
    sorted_items = sorted(cosine_sim.items(), key=lambda x: x[1], reverse=True)

    return sorted_items


### Below is the main function, creating the app demo for text search engine using the text embeddings.

if __name__ == "__main__":
    ### Text Search ###
    ### There will be Bonus marks of 10% for the teams that submit a URL for your deployed web app. 
    ### Bonus: You can also submit a publicly accessible link to the deployed web app.

    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*.
    """
    )

    model_type = st.sidebar.selectbox("Choose the model", ("25d", "50d", "100d"), index=1)

    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"
    # )
    st.text_input(
        label="Categories", key="categories", value="Flowers Colors Cars Weather Food"
    )
    print(st.session_state["categories"])
    print(type(st.session_state["categories"]))
    # print("Categories = ", categories)
    # st.session_state.categories = 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",
    )
    # st.session_state.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
        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 st.session_state.text_search:
        # Glove embeddings
        print("Glove Embedding")
        embeddings_metadata = {
            "embedding_model": "glove",
            "word_index_dict": word_index_dict,
            "embeddings": embeddings,
            "model_type": model_type,
        }
        with st.spinner("Obtaining Cosine similarity for Glove..."):
            sorted_cosine_sim_glove = get_sorted_cosine_similarity(
                st.session_state.text_search, 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(
                st.session_state.text_search, embeddings_metadata
            )

        # Results and Plot Pie Chart for Glove
        print("Categories are: ", st.session_state.categories)
        st.subheader(
            "Closest word I have between: "
            + st.session_state.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 Hongyan Liu and Yinxiu Wang(https://www.linkedin.com/in/your_id/ - Optional)"
        )