Spaces:
Sleeping
Sleeping
File size: 3,012 Bytes
9cd5782 09f0e17 9cd5782 09f0e17 9cd5782 09f0e17 9cd5782 09f0e17 9cd5782 09f0e17 9cd5782 09f0e17 9cd5782 09f0e17 bca9064 09f0e17 bca9064 09f0e17 9cd5782 09f0e17 9cd5782 09f0e17 9cd5782 09f0e17 9cd5782 09f0e17 9cd5782 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 |
import os
import pandas as pd
import numpy as np
import streamlit as st
import re
import pickle
import nltk
import string
import contractions
from nltk.corpus import stopwords
nltk.download("stopwords")
sw_list = stopwords.words('english')
def remove_tags(text):
return re.sub(re.compile('<.*?>'), '', text)
def lwr(text):
return text.lower()
def stopword(text):
return " ".join([word for word in text.split() if word not in sw_list])
def remove_punctuation(text):
return text.translate(str.maketrans('', '', string.punctuation))
def remove_contractions(text):
return contractions.fix(text)
def dec_vector(doc):
with open("Sentimental_Analysis_WV.pkl", 'rb') as file:
model = pickle.load(file)
doc = [word for word in doc.split() if word in model.wv.index_to_key]
return np.mean(model.wv[doc], axis=0)
def xvalue(text):
X = []
X.append(dec_vector(text))
return X
def preprocessed(text):
text = remove_tags(text)
text = lwr(text)
text = stopword(text)
text = remove_punctuation(text)
text = remove_contractions(text)
X = xvalue(text)
X = np.array(X)
return X
def clear_text():
st.session_state["text"] = ""
def main():
st.set_page_config(page_title="Sentiment Analysis AI", page_icon=":smiley:", layout="wide")
with open("Sentimental_Analysis_Word2Vec.pkl", 'rb') as file1:
rf = pickle.load(file1)
st.title('Sentiment Analysis AI')
st.markdown("""
<style>
.main {
background-color: #0a3d57;
padding: 20px;
border-radius: 10px;
}
.stButton > button {
background-color: #4CAF50;
color: white;
padding: 10px 24px;
border: none;
border-radius: 4px;
cursor: pointer;
}
.stButton > button:hover {
background-color: #45a049;
}
.stTextInput > div > input {
padding: 10px;
border: 2px solid #ccc;
border-radius: 4px;
}
</style>
""", unsafe_allow_html=True)
st.markdown("<div class='main'>", unsafe_allow_html=True)
st.header('Welcome to Sentiment Analysis AI')
st.subheader('Check if reviews or any text have a positive or negative sentiment instantly!')
text = st.text_input(
"Enter some text 👇", key="text", help="Type in any text to analyze its sentiment.")
col1, col2 = st.columns(2)
with col1:
if st.button('Classify'):
if text:
z = preprocessed(text)
if rf.predict(z)[0] == 1:
st.success("Positive")
else:
st.success("Negative")
else:
st.warning("Please enter some text to analyze.")
with col2:
st.button("Clear", on_click=clear_text)
st.markdown("</div>", unsafe_allow_html=True)
if __name__ == '__main__':
main()
|