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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') | |
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 | |
return round(prediction[0]) | |
iface = gr.Interface(fn=use, inputs="text", outputs="text") | |
iface.launch() | |