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File size: 1,999 Bytes
2a982df | 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 | import streamlit as st
from keras.models import load_model
from keras.preprocessing.sequence import pad_sequences
from streamlit_lottie import st_lottie
import requests
import pickle
col1, col2 = st.columns([1, 1])
with col1:
# Load the saved tokenizer
with open('tokenizer.pickle', 'rb') as handle:
tokenizer = pickle.load(handle)
# Load the saved MAX_SEQUENCE_LENGTH
with open('max_sequence_length.txt', 'r') as f:
MAX_SEQUENCE_LENGTH = int(f.read())
# Load the saved model
model = load_model('Sentiment_CNN_model.h5')
# Define the tokenizer function
def tokenize_text(text):
# Tokenize text
x_test = pad_sequences(tokenizer.texts_to_sequences([text]), maxlen=MAX_SEQUENCE_LENGTH)
return x_test
# Streamlit app
st.title('Twitter Sentiment Analysis')
user_input = st.text_input("Enter a tweet for sentiment analysis")
if st.button('Submit'):
processed_input = tokenize_text(user_input)
prediction = model.predict(processed_input)
if prediction >= 0.7:
st.success(f"Positive sentiment 😊")
st.slider('Sentiment Score', 0,10, int (prediction*10))
elif 0.3 < prediction < 0.7: # Adjusted the condition to check for values between 0.4 and 0.7
st.info(f"Neutral sentiment 🙂")
st.slider('Sentiment Score', 0,10, int(prediction*10))
else:
st.warning(f"Negative sentiment 😖")
st.slider('Sentiment Score', 0,10, int(prediction*10))
with col2:
def load_lottieurl(url: str):
r = requests.get(url)
if r.status_code != 200:
return None
return r.json()
lottie_hello = load_lottieurl("https://lottie.host/22856a7c-7ef0-453d-81ea-dc5842ab763a/p0C048YfUz.json")
st_lottie(
lottie_hello,
speed=1,
reverse=False,
loop=True,
quality="high",
width=300,
height=300,
key=None,
)
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