import gradio as gr from gradio.components import Text import joblib import clean import nltk nltk.download('wordnet') import numpy as np import language_detection print("all imports worked") # Load pre-trained model model = joblib.load('model_joblib.pkl') print("model load ") tf = joblib.load('tf_joblib.pkl') print("tfidf load ") # Define function to predict whether sentence is abusive or not def predict_abusive_lang(text): print("original text ", text) lang = language_detection.en_hi_detection(text) print("language detected ", lang) if lang=='eng': cleaned_text = clean.text_cleaning(text) print("cleaned text ", text) text = tf.transform([cleaned_text]) print("tfidf transformation ", text) prediction = model.predict(text) print("prediction ", prediction) if len(prediction)!=0 and prediction[0]==0: return ["Not Abusive", cleaned_text] elif len(prediction)!=0 and prediction[0]==1: return ["Abusive",cleaned_text] else : return ["Please write something in the comment box..","No cleaned text"] elif lang=='hi': print("using hugging face api") return ["Hindi Text abusive part coming soon.....","No cleaned text"] else : return ["Unknown language","No cleaned text"] # text = '":::::: 128514 - & % ! @ # $ % ^ & * ( ) _ + I got blocked for 30 minutes, you got blocked for more than days. You is lost. www.google.com, #happydiwali, @amangupta And I don\'t even know who the fuck are you. It\'s a zero! \n"' # predict_abusive_lang(text) # Define the GRADIO output interfaces output_interfaces = [ gr.outputs.Textbox(label="Result"), gr.outputs.Textbox(label="Cleaned text") ] app = gr.Interface(predict_abusive_lang, inputs='text', outputs=output_interfaces, title="Abuse Classifier", description="Enter a sentence and the model will predict whether it is abusive or not.") #Start the GRADIO app app.launch()