app.py

import gradio as gr
import numpy as np
import pandas as pd
from datetime import datetime
import os
import re

from huggingface_hub import hf_hub_url, cached_download
from gensim.models.fasttext import load_facebook_model

ACCESS_KEY = os.environ.get('ACCESS_KEY')


# Setup model
url = hf_hub_url(repo_id="simonschoe/call2vec", filename="model.bin")
cached_download(url)
model = load_facebook_model(cached_download(url))

def semantic_search(_input, n):
    """ Perform semantic search """

    _input = re.split('[,;\n]', _input)
    _input = [s.strip().lower().replace(' ', '_') for s in _input if s]

    if _input[0] != ACCESS_KEY:
        with open('log.txt', 'a') as f:
            f.write(str(datetime.now()) + '+++' + '___'.join(_input) + '\n')

    if len(_input) > 1:
        avg_input = np.stack([model.wv[w] for w in _input], axis=0).mean(axis=0)
        nearest_neighbours = model.wv.most_similar(positive=avg_input, topn=n)
        frequencies = [model.wv.get_vecattr(nn[0], 'count') for nn in nearest_neighbours]
    else:
        nearest_neighbours = model.wv.most_similar(positive=_input[0], topn=n)
        frequencies = [model.wv.get_vecattr(nn[0], 'count') for nn in nearest_neighbours]
    
    if _input[0] == ACCESS_KEY:
        with open('log.txt', 'r') as f:
            prompts = f.readlines()
            prompts = [p.strip().split('+++') for p in prompts]
        result = pd.DataFrame(prompts,
                              columns=['Time', 'Prompt'])
    else:
        result = pd.DataFrame([(a[0],a[1],b) for a,b in zip(nearest_neighbours, frequencies)],
                              columns=['Token', 'Cosine Similarity', 'Corpus Frequency'])
    
    result.to_csv('result.csv')
    return result, 'result.csv', '\n'.join(_input)

app = gr.Blocks()

with app:
    gr.Markdown("# Call2Vec")
    gr.Markdown("## Semantic Search in Quarterly Earnings Conference Calls")
    with gr.Row():
        with gr.Column():
            text_in = gr.Textbox(lines=1, placeholder="Insert text", label="Search Query")
            with gr.Row():
                n = gr.Slider(value=50, minimum=5, maximum=500, step=5, label="Number of Neighbours")
                compute_bt = gr.Button("Search")
            df_out = gr.Dataframe(interactive=False)
            f_out = gr.File(interactive=False, label="Download")
        with gr.Column():
            gr.Markdown(
                """
                #### Project Description
                Call2Vec is a [fastText](https://fasttext.cc/) word embedding model trained via [Gensim](https://radimrehurek.com/gensim/). It maps each token in the vocabulary into a dense, 300-dimensional vector space, designed for performing semantic search.
                The model is trained on a large sample of quarterly earnings conference calls, held by U.S. firms during the 2006-2022 period. In particular, the training data is restriced to the (rather sponentous) executives' remarks of the Q&A section of the call. The data has been preprocessed prior to model training via stop word removal, lemmatization, named entity masking, and coocurrence modeling.
                """
            )
            gr.Markdown(
                """
                #### App usage
                The model is intented to be used for **semantic search**: It encodes the search query (entered in the textbox on the right) in a dense vector space and finds semantic neighbours, i.e., token which frequently occur within similar contexts in the underlying training data.
                The model allows for two use cases:
                1. *Single Search:* The input query consists of a single word. When provided a bi-, tri-, or even fourgram, the quality of the model output depends on the presence of the query token in the model's vocabulary. N-grams should be concated by an underscore (e.g., "machine_learning" or "artifical_intelligence").
                2. *Multi Search:* The input query may consist of several words or n-grams, seperated by comma, semi-colon or newline. It then computes the average vector over all inputs and performs semantic search based on the average input token.
                """
            )
            gr.Examples(
                examples = [
                    ["transformation", 20],
                    ["climate_change", 50],
                    ["risk, political_risk, uncertainty", 250],
                ],
                inputs = [text_in, n],
                outputs = [df_out, f_out, text_in],
                fn = semantic_search,
                cache_examples=True
            )
    gr.Markdown(
        """
        <p style="text-align: center;">
            Call2Vec by X and Y
            <br>
            <img 'id="visitor-badge" alt="visitor badge" src="https://visitor-badge.glitch.me/badge?page_id=simonschoe.call2vec&left_color=green&right_color=blue" />
        </p>
        """
    )
    compute_bt.click(semantic_search, inputs=[text_in, n], outputs=[df_out, f_out, text_in])

app.launch()