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| 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 | |
| import requests | |
| 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 ") | |
| def query(payload): | |
| API_URL = "https://api-inference.huggingface.co/models/Hate-speech-CNERG/hindi-abusive-MuRIL" | |
| headers = {"Authorization": "Bearer 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"} | |
| response = requests.post(API_URL, headers=headers, json=payload) | |
| return response.json() | |
| # 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 ["NA", cleaned_text] | |
| elif len(prediction)!=0 and prediction[0]==1: | |
| return ["AB",cleaned_text] | |
| else : | |
| return ["Please write something in the comment box..","No cleaned text"] | |
| elif lang=='hi': | |
| print("using hugging face api") | |
| output = query({ | |
| "inputs": text#"खान चाचा को मेरा सला" | |
| }) | |
| print(output, len(output)) | |
| # if(len(output)) | |
| l_0 = float(output[0][0]['score']) | |
| l_1 = float(output[0][1]['score']) | |
| if output[0][0]['label']=='LABEL_1' : | |
| if l_0>l_1: | |
| return ["AB",text] | |
| else : | |
| return ["NA",text] | |
| else : | |
| return ["UN","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() |