app.py

#!/usr/bin/env python
# coding: utf-8

import gradio as gr
import numpy as np
import requests
from transformers import AutoModelForSequenceClassification, AutoTokenizer, TextClassificationPipeline, pipeline
from langdetect import detect
from matplotlib import pyplot as plt
import imageio

# Load the model
model = AutoModelForSequenceClassification.from_pretrained("saved_model") 
tokenizer = AutoTokenizer.from_pretrained("saved_model")
pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer)

# Function called by the UI
def attribution(text):
    
    # Clean the plot
    plt.clf()
    
    # Detect the language
    language = detect(text)
     
    # Translate the input in german if necessary
    if language == 'fr':
        translator = pipeline("translation", model="Helsinki-NLP/opus-mt-fr-de")
        translatedText = translator(text[0:1000])
        text = translatedText[0]["translation_text"]
    elif language != 'de':
        return "The language is not recognized, it must be either in German or in French.", None
    
    # Set the bars of the bar chart
    bars = ""
    if language == 'fr':
        bars = ("DDPS", "DFI", "AS-MPC", "DFJP", "DEFR", "DETEC", "DFAE", "Parl", "ChF", "DFF", "AF", "TF")
    else:
        bars = ("VBS", "EDI", "AB-BA", "EJPD", "WBF", "UVEK", "EDA", "Parl", "BK", "EFD", "BV", "BGer")

    # Make the prediction with the 1000 first characters 
    results = pipe(text[0:1000], return_all_scores=True)
    rates = [row["score"] for row in results[0]]
    
    # Bar chart
    y_pos = np.arange(len(bars))
    plt.barh(y_pos, rates)
    plt.yticks(y_pos, bars)
    
    # Set the output text
    name = ""    
    maxRate = np.max(rates)
    maxIndex = np.argmax(rates)

    # ML model not sure if highest probability < 60%
    if maxRate < 0.6:
        # de / fr
        if language == 'de':
            name = "Das ML-Modell ist nicht sicher. Das Departement könnte sein : \n\n"
        else:
            name = "Le modèle ML n'est pas sûr. Le département pourrait être : \n\n"
        i = 0
        # Show each department that has a probability > 10%
        while i == 0:
            if rates[maxIndex] >= 0.1:
                name = name + "\t" + str(rates[maxIndex])[2:4] + "%" + "\t\t\t\t\t" + bars[maxIndex] + "\n"
                rates[maxIndex] = 0
                maxIndex = np.argmax(rates)
            else:
                i = 1
    # ML model pretty sure, show only one department
    else:
        name =  str(maxRate)[2:4] + "%" + "\t\t\t\t\t\t" + bars[maxIndex]
    
    # Save the bar chart as png and load it (enables better display)
    plt.savefig('rates.png')
    im = imageio.imread('rates.png')

    return name, im
 

# display the UI
interface = gr.Interface(fn=attribution, 
                         inputs=[gr.inputs.Textbox(lines=20, placeholder="Geben Sie bitte den Titel und den Sumbmitted Text des Vorstoss ein.\nVeuillez entrer le titre et le Submitted Text de la requête.")],
                          outputs=['text', 'image'])
interface.launch()