app_scatter.py

import gensim.downloader 
import gradio as gr
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
model = gensim.downloader.load("glove-wiki-gigaword-50")

# Function to reduce dimensions
def reduce_dimensions(data, method='PCA'):
    if method == 'PCA':
        model = PCA(n_components=2)
    elif method == 'TSNE':
        model = TSNE(n_components=2, learning_rate='auto', init='random', perplexity=3)
    return model.fit_transform(data)

description = """
### Word Embedding Demo App
Universidade Federal de São Paulo - Escola Paulista de Medicina

The output is Word3 + (Word2 - Word1)

Credits:  
* Gensim
* Glove
"""

Word1 = gr.Textbox()
Word2 = gr.Textbox()
Word3 = gr.Textbox()
label = gr.Label(show_label=True, label="Word4")
sp = gr.ScatterPlot(x="x", y="y", color="color", label="label")


def inference(word1, word2, word3):
    output = model.similar_by_vector(model[word3] + model[word2] - model[word1])
    print(output)
    word_list = [word1, word2, word3]
    word_list.extend([x for x,y in [item for item in output[:4]]])
    words = {key: model[key] for key in word_list}
    data = np.concatenate([x[np.newaxis, :] for x in words.values()], axis=0)
    print(data.shape)
    labels = words.keys()
    reduced_data_pca = reduce_dimensions(data, method='PCA')
    print(reduced_data_pca.shape)
    #'''
    df = pd.DataFrame({
        "x": reduced_data_pca[:, 0], 
        "y": reduced_data_pca[:, 1],
        "color": [x for x in labels][:len(data)]
        #"label": ["W1", "W2", "W3", "W4", "W5", "W6", "W7"][:len(data)]
    })
    #'''
    return df

examples = [
    ["woman", "man", "aunt"],  
    ["woman", "man", "girl"],
    ["woman", "man", "granddaughter"],
]

iface = gr.Interface(
    fn=inference,
    inputs=[Word1, Word2, Word3],
    outputs=sp,
    description=description,
    examples=examples
    )

iface.launch()