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| import os | |
| from time import sleep | |
| print("Upgrade and Install...") | |
| os.system('pip install tensorflow numpy gradio keras tensorflow-cpu') | |
| os.system('pip install --upgrade tensorflow numpy gradio keras nvidia-cuda-toolkit libcudnn8 tensorflow-cpu') | |
| os.system('rm -rf ~/.keras ~/.cache') | |
| sleep(5) | |
| 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 = keras.layers.TFSMLayer("sentiment_mini-test", call_endpoint='serving_default') | |
| 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="<center>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.</center>", | |
| article="\nRSA v0.1.2: @2.3M Params w/ 96.5% acc. & 388MB input dataset + 1.59MB output dataset. Trained on <a href='https://www.kaggle.com/datasets/ilhamfp31/yelp-review-dataset'>this Kaggle dataset</a>", | |
| examples=[ | |
| ["I went there today! The cut was terrible! I had an awful experience. The lady that cut my hair was nice but she wanted to leave early so she made a disaster on 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 Valentine's 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() | |