app.py

import gradio as gr
from transformers import pipeline

print("Loading models...")
quality_pipe = pipeline("text-classification", model="FareehaAly/fator-argument-quality", top_k=None)
fallacy_pipe = pipeline("text-classification", model="FareehaAly/fator-fallacy-detector", top_k=None)
print("Models loaded!")

def predict_quality(text):
    return quality_pipe(text[:512])

def predict_fallacy(text):
    return fallacy_pipe(text[:512])

with gr.Blocks() as demo:
    gr.Markdown("## FATOR NLP API - Running")
    with gr.Row():
        txt = gr.Textbox(label="Input text", scale=4)
    with gr.Row():
        btn_q = gr.Button("Quality")
        btn_f = gr.Button("Fallacy")
    out_q = gr.JSON(label="Quality Result")
    out_f = gr.JSON(label="Fallacy Result")
    btn_q.click(predict_quality, inputs=txt, outputs=out_q, api_name="quality")
    btn_f.click(predict_fallacy, inputs=txt, outputs=out_f, api_name="fallacy")

demo.launch(server_name="0.0.0.0", server_port=7860)