import gradio as gr import tensorflow from tensorflow.keras.models import load_model import prepro import numpy as np import nltk def classify(text): nltk.download('stopwords') model= load_model('nlp3.h5') X= prepro.preprocess(text) prediction = model.predict(np.array(X)) # return prediction if(prediction<=0.4): return "Looks like you are reading negative content. Some words sound negative in context." elif(prediction>0.4 and prediction<=0.6): return "Sounds Neutral. Speaks generally and not biased towards any value." else : return "Sounds Positive. Giving a good impression to start reading this stuff. " iface= gr.Interface( inputs=[gr.inputs.Textbox(lines=5, label="Context", placeholder="Type a sentence or paragraph here.")], outputs=[gr.outputs.Textbox(label="Prediction")], fn=classify, title='WATCHA-READIN', theme='dark-peach' ) iface.launch(share=True)