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import streamlit as st
import tensorflow as tf
from tensorflow.keras.preprocessing.sequence import pad_sequences
import numpy as np
from nltk.stem.porter import PorterStemmer
from nltk.corpus import stopwords
import re
import pickle
import nltk
nltk.download('stopwords')
# Load your trained model
model = tf.keras.models.load_model('saved_model')
# Load your tokenizer
with open('tokenizer_embd.pickle', 'rb') as handle:
tokenizer = pickle.load(handle)
# Function to clean and preprocess text
def cleaning(text):
text = re.sub(r'[^a-zA-Z]', ' ', text)
text = text.lower()
text = text.split()
ps = PorterStemmer()
text = [ps.stem(word) for word in text if word not in stopwords.words("english")]
text = ' '.join(text)
return text
# Set Streamlit app layout
st.title("Sentiment Analysis")
# st.markdown("Enter a text and see if it's Positive or Negative!")
# Text input for user
orginal_text = st.text_area("Enter a text and see if it's Positive or Negative!")
# Clean and preprocess text
text = cleaning(orginal_text)
if st.button("Predict"):
# Tokenize and pad the input text
sequences = tokenizer.texts_to_sequences([text])
padded_sequence = pad_sequences(sequences, maxlen=40, padding='pre')
# Make a prediction using the model
prediction = model.predict(padded_sequence)
# Determine sentiment
predicted_sentiment = 'Positive' if prediction > 0.5 else 'Negative'
sentiment_positive = prediction > 0.5
# Display result
# st.subheader("Prediction:")
st.write(f"Sentiment: {predicted_sentiment}")
if sentiment_positive:
st.image("https://media.giphy.com/media/50fuVHMGUVszu/giphy.gif", width=300)
else:
st.image("https://media.giphy.com/media/P2a7oxnUULoys/giphy.gif", width=300)
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