import os os.system('pip install tensorflow') import tensorflow as tf from tensorflow import keras import numpy as np import gradio as gr tokenizer = tf.keras.preprocessing.text.Tokenizer() #Reads Text Inputs Here f=open('Inputs.txt','r') inputs = f.read().split('\n') f.close() corpus = inputs tokenizer.fit_on_texts(corpus) sequences = tokenizer.texts_to_sequences(corpus) max_length = max([len(s) for s in sequences]) # Load your saved model model = tf.keras.models.load_model('sentiment_mini-test') model.summary() def use(input_text): # Preprocess the input text sequences = tokenizer.texts_to_sequences([input_text]) sequences = tf.keras.preprocessing.sequence.pad_sequences(sequences, padding='post', maxlen=max_length) # Make a prediction on the input text prediction = model.predict(sequences)[0] # Print the prediction if prediction[0]<0.3: return "That's Negative! (" + str(round(round(1-prediction[0],2)*100,1)) + "% confidence)", prediction[0] elif prediction[0]>0.3: return "That's Positive! (" + str(round(round(prediction[0],2)*100,1)) + "% confidence)", prediction[0] else: return "That's Neutral!", prediction[0] iface = gr.Interface(fn=use, inputs=gr.Textbox(lines=8, placeholder="Type Something Awesome..."), outputs=[gr.Textbox(lines=3, placeholder="Waiting For Magic..."),"number"], title="Use RSA (Review Sentiment Analysis) v0.1.2", description="
This is an NLP model that accepts a text string as input and simply outputs if the string is mean or nice with about 96.5% accuracy. It also provides you with a score of how positive or negative it is.
", article="\nRSA v0.1.2: @2.3M Params w/ 96.5% acc. & 388MB input dataset + 1.59MB output dataset. Trained on this Kaggle dataset", examples=[ ["I went there today! The cut was terrible! I have an awful experience. They lady that cut my hair was nice but she wanted to leave early so she made a disaster in my head!"], ["Yes! Awesome soy cap, scone, and atmosphere. Nice place to hang out & read, and free WiFi with no login procedure."], ["Overpriced, salty and overrated!!! Why this place is so popular I will never understand."], ["This Valentines Day I ordered a pizza for my boyfriend and asked that they make a heart on it out of green peppers. The pizza was great, the heart was perfect, and he loved it!"] ]) iface.launch()