app.py

# %load miniproject1_part4-2-1.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)
    """
    # Compute cosine similarity
    dot_product = np.dot(x, y)
    norm_x = np.linalg.norm(x)
    norm_y = np.linalg.norm(y)
    cosine_sim = dot_product / (norm_x * norm_y)

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

    # Return exponentiated cosine similarity
    return exp_cosine_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


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

    # 修改的
    embeddings_temp = "embeddings_50d_temp.npy"
    # embeddings_temp = "embeddings_" + str(model_type) + "_temp.npy"

    # 修改的
    word_index_temp = "word_index_dict_50d_temp.pkl"
    # 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 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)
    """
    words = sentence.split()  # Step 1: Split sentence into words
    embedding_sum = np.zeros(int(model_type.split("d")[0]))
    valid_word_count = 0

    for word in words:  # Step 2: Get embeddings for each word
        word_embedding = get_glove_embeddings(word, word_index_dict, embeddings, model_type)
        if np.any(word_embedding):  # Only consider valid embeddings
            embedding_sum += word_embedding
            valid_word_count += 1

    if valid_word_count > 0:  # Step 4: Divide by number of words
        averaged_embedding = embedding_sum / valid_word_count
    else:
        averaged_embedding = np.zeros(int(model_type.split("d")[0]))

    return averaged_embedding  # Step 5: Return averaged embeddings

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(embedings_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 = 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(st.session_state.text_search,
                                                            word_index_dict,
                                                            embeddings, model_type)

        for category in categories:
            # Get embedding for category
            category_embedding = averaged_glove_embeddings_gdrive(category, word_index_dict, embeddings, model_type)
            # Compute cosine similarity
            cos_sim = cosine_similarity(input_embedding, category_embedding)
            cosine_sim[category] = cos_sim

    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 = ", st.session_state.text_search)
        if model_name:
            input_embedding = get_sentence_transformer_embeddings(st.session_state.text_search, model_name=model_name)
        else:
            input_embedding = get_sentence_transformer_embeddings(st.session_state.text_search)
        for category in categories:
            # Update category embeddings if category not found

            if category not in category_embeddings:
                update_category_embeddings(embeddings_metadata)
                category_embeddings = st.session_state["cat_embed_" + model_name]

            # Compute cosine similarity
            category_embedding = category_embeddings[category]
            cos_sim = cosine_similarity(input_embedding, category_embedding)
            cosine_sim[category] = cos_sim

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

    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 = 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(list(sorted_cosine_scores_items.values()))
    categories_sorted = list(sorted_cosine_scores_items.keys())

    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
    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:
        # modified
        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*.
"""
)


if 'categories' not in st.session_state:
    st.session_state['categories'] = "Flowers Colors Cars Weather Food"
if 'text_search' not in st.session_state:
    st.session_state['text_search'] = "Roses are red, trucks are blue, and Seattle is grey right now"


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


# categories of user input
user_categories = st.text_input(
    label="Categories", value=st.session_state.categories
)

st.session_state.categories = user_categories

# st.text_input(
#     label="Categories", key="categories", value="Flowers Colors Cars Weather Food"
# )

# Categories = st.session_state.get('categories', "Flowers Colors Cars Weather Food")


print(st.session_state.get("categories"))
# print(st.session_state["categories"])

print(type(st.session_state.get("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")
user_text_search = st.text_input(
    label="Input your sentence",
    value=st.session_state.text_search,

)

st.session_state.text_search = user_text_search
# 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,
        "text_search": st.session_state.text_search
    }
    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": "",
                            "text_search": st.session_state.text_search }
    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: ", 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)

    st.write(f"Closest category using GloVe embeddings : {list(sorted_cosine_sim_glove.keys())[0]}")
    st.write(
        f"Closest category using Sentence Transformer embeddings : {list(sorted_cosine_sim_transformer.keys())[0]}")

    plot_alatirchart(
        {
            "glove_" + str(model_type): sorted_cosine_sim_glove,
            "sentence_transformer_384": sorted_cosine_sim_transformer,
        }
    )

    st.write("")
    st.write(
        "Demo developed by [V50](https://huggingface.co/spaces/ericlkc/V50)"
    )