app.py

import streamlit as st
import pandas as pd
import torch

from backend import inference
from backend.config import MODELS_ID, QA_MODELS_ID, SEARCH_MODELS_ID
from backend.utils import load_gender_data

st.title('Demo using Flax-Sentence-Tranformers')

st.sidebar.title('Tasks')
menu = st.sidebar.radio("", options=["Sentence Similarity", "Asymmetric QA", "Search / Cluster",
                                     "Gender Bias Evaluation"], index=0)

st.markdown('''

Hi! This is the demo for the [flax sentence embeddings](https://huggingface.co/flax-sentence-embeddings) created for the **Flax/JAX community week 🤗**. 
We trained three general-purpose flax-sentence-embeddings models: a **distilroberta base**, a **mpnet base** and a **minilm-l6**. 
The models were trained on a dataset comprising of [1 Billion+ training corpus](https://huggingface.co/flax-sentence-embeddings/all_datasets_v4_MiniLM-L6#training-data) with the v3 setup.

In addition, we trained [20 models](https://huggingface.co/flax-sentence-embeddings) focused on general-purpose, QuestionAnswering and Code search.
You can view our models [here](https://huggingface.co/flax-sentence-embeddings)

## Contributions

- 20 sentence embedding models that can be utilized for Sentence Simliarity / Asymmetric QA / Search / Clustering.
- 8 Datasets from Stackexchange and StackOverflow, PAWS, Gender Evaluation formatted and uploaded to HuggingFace Hub.
- Achieve SOTA on multiple general purpose Sentence Similarity evaluation tasks by utilizing large TPU memory to maximize
 customized Contrastive Loss.
- Gender Bias Demonstration that explores inherent bias in general purpose datasets.
- Search / Clustering Demonstration that showcases real-world use-cases for Sentence Embeddings.

''')

if menu == "Sentence Similarity":
    st.header('Sentence Similarity')
    st.markdown('''
**Instructions**: You can compare the similarity of the main text with other texts of your choice. In the background, we'll create an embedding for each text, and then we'll use the cosine similarity function to calculate a similarity metric between our main sentence and the others.

For more cool information on sentence embeddings, see the [sBert project](https://www.sbert.net/examples/applications/computing-embeddings/README.html).
''')
    select_models = st.multiselect("Choose models", options=list(MODELS_ID), default=list(MODELS_ID)[0])

    anchor = st.text_input(
        'Please enter here the main text you want to compare:'
    )

    n_texts = st.number_input(
        f'''How many texts you want to compare with: '{anchor}'?''',
        value=2,
        min_value=2)

    inputs = []

    for i in range(int(n_texts)):
        input = st.text_input(f'Text {i + 1}:')

        inputs.append(input)

    if st.button('Tell me the similarity.'):
        results = {model: inference.text_similarity(anchor, inputs, model, MODELS_ID) for model in select_models}
        df_results = {model: results[model] for model in results}

        index = [f"{idx + 1}:{input[:min(15, len(input))]}..." for idx, input in enumerate(inputs)]
        df_total = pd.DataFrame(index=index)
        for key, value in df_results.items():
            df_total[key] = [ts.item() for ts in torch.nn.functional.softmax(torch.from_numpy(value['score'].values))]

        st.write('Here are the results for selected models:')
        st.write(df_total)
        st.write('Visualize the results of each model:')
        st.line_chart(df_total)
elif menu == "Asymmetric QA":
    st.header('Asymmetric QA')
    st.markdown('''
**Instructions**: You can compare the Answer likeliness of a given Query with answer candidates of your choice. In the background, we'll create an embedding for each answer, and then we'll use the cosine similarity function to calculate a similarity metric between our query sentence and the others.
`mpnet_asymmetric_qa` model works best for hard-negative answers or distinguishing similar queries due to separate models applied for encoding questions and answers.

For more cool information on sentence embeddings, see the [sBert project](https://www.sbert.net/examples/applications/computing-embeddings/README.html).
''')

    select_models = st.multiselect("Choose models", options=list(QA_MODELS_ID), default=list(QA_MODELS_ID)[0])

    anchor = st.text_input(
        'Please enter here the query you want to compare with given answers:',
        value="What is the weather in Paris?"
    )

    n_texts = st.number_input(
        f'''How many answers you want to compare with: '{anchor}'?''',
        value=3,
        min_value=2)

    inputs = []

    defaults = ["It is raining in Paris right now with 70 F temperature.", "What is the weather in Berlin?", "I have 3 brothers."]
    for i in range(int(n_texts)):
        input = st.text_input(f'Answer {i + 1}:', value=defaults[i] if i < len(defaults) else "")

        inputs.append(input)

    if st.button('Tell me Answer likeliness.'):
        results = {model: inference.text_similarity(anchor, inputs, model, QA_MODELS_ID) for model in select_models}
        df_results = {model: results[model] for model in results}

        index = [f"{idx + 1}:{input[:min(15, len(input))]}..." for idx, input in enumerate(inputs)]
        df_total = pd.DataFrame(index=index)
        for key, value in df_results.items():
            df_total[key] = [ts.item() for ts in torch.nn.functional.softmax(torch.from_numpy(value['score'].values))]

        st.write('Here are the results for selected models:')
        st.write(df_total)
        st.write('Visualize the results of each model:')
        st.line_chart(df_total)

elif menu == "Search / Cluster":
    st.header('Search / Cluster')
    st.markdown('''
**Instructions**: Make a query for anything related to "Python" and the model you choose will return you similar queries.

For more cool information on sentence embeddings, see the [sBert project](https://www.sbert.net/examples/applications/computing-embeddings/README.html).
''')

    select_models = st.multiselect("Choose models", options=list(SEARCH_MODELS_ID), default=list(SEARCH_MODELS_ID)[0])

    anchor = st.text_input(
        'Please enter here your query about "Python", we will look for similar ones:',
        value="How do I sort a dataframe by column"
    )

    n_texts = st.number_input(
        f'''How many similar queries you want?''',
        value=3,
        min_value=2)

    if st.button('Give me my search.'):
        results = {model: inference.text_search(anchor, n_texts, model, QA_MODELS_ID) for model in select_models}
        st.table(pd.DataFrame(results[select_models[0]]).T)

    if st.button('3D Clustering of search result using T-SNE on generated embeddings'):
        st.write("Currently only works at local due to Spaces / plotly integration.")
        st.write("Demonstration : https://gyazo.com/1ff0aa438ae533de3b3c63382af7fe80")
        # fig = inference.text_cluster(anchor, 1000, select_models[0], QA_MODELS_ID)
        # fig.show()

elif menu == "Gender Bias Evaluation":
    st.header("Gender Bias Evaluation")
    st.markdown('''
**Instructions**: Here we can observe **inherent gender bias** in training set via random sampling of the sentences.

Input 3 texts, one without any mention of gender for target occupation and 2 others with gendered pronouns. 

Hopefully the evaluation performed here can proceed towards improving Gender-neutrality of datasets.

For more cool information on sentence embeddings, see the [sBert project](https://www.sbert.net/examples/applications/computing-embeddings/README.html).
''')

    select_models = st.multiselect("Choose models", options=list(MODELS_ID), default=list(MODELS_ID)[0])

    base_text = st.text_input("Gender Neutral Text", "President of the United States promised relief to Hurricane survivors.")
    male_text = st.text_input("Male-assumed Text", "He promised relief to Hurricane survivors.")
    female_text = st.text_input("Female-assumed Text", "She promised relief to Hurricane survivors.")

    enter = st.button("Compare")
    if enter:
        results = {model: inference.text_similarity(base_text, [male_text, female_text], model, MODELS_ID) for model in select_models}

        index = ["male", "female", "gender_bias"]
        df_total = pd.DataFrame(index=index)
        for key, value in results.items():
            softmax = [ts.item() for ts in torch.nn.functional.softmax(torch.from_numpy(value['score'].values))]
            if softmax[0] > softmax[1]:
                gender = "male"
            elif abs(softmax[0] - softmax[1]) < 1e-2:
                gender = "neutral"
            else:
                gender = "female"
            softmax.append(gender)
            df_total[key] = softmax

        st.write('Here are the results for selected models:')
        st.write(df_total)