app.py

import gradio as gr
import numpy as np
import pickle
from sentence_transformers import SentenceTransformer


#css_code='body {background-image:url("https://picsum.photos/seed/picsum/200/300");} div.gradio-container {background: white;}'


categories = ["Censorship","Development","Digital Activism","Disaster","Economics & Business","Education","Environment","Governance","Health","History","Humanitarian Response","International Relations","Law","Media & Journalism","Migration & Immigration","Politics","Protest","Religion","Sport","Travel","War & Conflict","Technology_Science","Women&Gender_LGBTQ+_Youth","Freedom_of_Speech_Human_Rights","Literature_Arts&Culture"]
model = SentenceTransformer('sentence-transformers/LaBSE')
with open('models/MLP_classifier_average_en.pkl', 'rb') as f:
    classifier = pickle.load(f)

def get_embedding(text):
    if text is None:
        text = ""
    return model.encode(text)

def get_categories(y_pred):
    indices = []
    for idx, value in enumerate(y_pred):
        if value == 1:
            indices.append(idx)
    cats = [categories[i] for i in indices]
    return cats

def generate_output(article):
    paragraphs = article.split("\n")
    embdds = []
    for par in paragraphs:
        embdds.append(get_embedding(par))
    embedding = np.average(embdds, axis=0)

    #y_pred = classifier.predict_proba(embedding.reshape(1, 768))
    y_pred = classifier.predict(embedding.reshape(1, 768))
    y_pred = y_pred.flatten()
    classes = get_categories(y_pred)

    return (classes, "clustering tbd")

# with gr.Blocks() as demo:
#     with gr.Row():
#         # column for input
#         with gr.Column():
#             input_text = gr.Textbox(lines=6, placeholder="Insert text of the article here...", label="Article"),
#             submit_button = gr.Button("Submit")
#             clear_button = gr.Button("Clear") 

#         # column for output
#         with gr.Column():
#             output_classification = gr.Textbox(lines=1, label="Article category")
#             output_topic_discovery = gr.Textbox(lines=5, label="Topic discovery")

    #submit_button.click(generate_output, inputs=input_text, outputs=[output_classification, output_topic_discovery])
demo = gr.Interface(fn=generate_output, 
    inputs=gr.Textbox(lines=6, placeholder="Insert text of the article here...", label="Article"), 
    outputs=[gr.Textbox(lines=1, label="Category"), gr.Textbox(lines=5, label="Topic discovery")], 
    title="Article classification & topic discovery demo",
    flagging_options=["Incorrect"],
    theme=gr.themes.Base())
    #css=css_code)

demo.launch()