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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()
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