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import streamlit as st
import transformers
import torch
# Load the model and tokenizer
model = transformers.AutoModelForSequenceClassification.from_pretrained("DeeeTeeee01/mytest_trainer_roberta-base")
tokenizer = transformers.AutoTokenizer.from_pretrained("DeeeTeeee01/mytest_trainer_roberta-base")
# Define the function for sentiment analysis
@st.cache_resource
def predict_sentiment(text):
# Load the pipeline
pipeline = transformers.pipeline("sentiment-analysis", model = "DeeeTeeee01/mytest_trainer_roberta-base", tokenizer= "DeeeTeeee01/mytest_trainer_roberta-base")
# Predict the sentiment
prediction = pipeline(text)
sentiment = prediction[0]["label"]
score = prediction[0]["score"]
return sentiment, score
# Setting the page configurations
st.set_page_config(
page_title="Sentiment Analysis App",
page_icon=":smile:",
layout="wide",
initial_sidebar_state="auto",
)
# Add description and title
st.write("""
# Twit Analyzer
Please type your text and click the Predict button to know if your text has a positive, negative or neutral sentiment!
""")
# Add image
image = st.image("sentiment.jpeg", width=400)
# Get user input
text = st.text_input("Type here:")
# Add Predict button
predict_button = st.button("Predict")
# Define the CSS style for the app
st.markdown(
"""
<style>
body {
background: linear-gradient(to right, #4e79a7, #86a8e7);
color: lightblue;
}
h1 {
color: #4e79a7;
}
</style>
""",
unsafe_allow_html=True
)
# Show sentiment output
if predict_button and text:
sentiment, score = predict_sentiment(text)
if sentiment == "Positive":
st.success(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")
elif sentiment == "Negative":
st.error(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")
else:
st.warning(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")
# import streamlit as st
# import transformers
# import torch
# # Load the model and tokenizer
# model = transformers.AutoModelForSequenceClassification.from_pretrained("DeeeTeeee01/twitter-xlm-roberta-base-sentiment_dee")
# tokenizer = transformers.AutoTokenizer.from_pretrained("DeeeTeeee01/twitter-xlm-roberta-base-sentiment_dee")
# # Define the function for sentiment analysis
# @st.cache_resource
# def predict_sentiment(text):
# # Load the pipeline.
# pipeline = transformers.pipeline("sentiment-analysis")
# # Predict the sentiment.
# prediction = pipeline(text)
# sentiment = prediction[0]["label"]
# score = prediction[0]["score"]
# return sentiment, score
# # Setting the page configurations
# st.set_page_config(
# page_title="Sentiment Analysis App",
# page_icon=":smile:",
# layout="wide",
# initial_sidebar_state="auto",
# )
# # Add description and title
# st.write("""
# # Predict if your text is Positive, Negative or Nuetral ...
# Please type your text and press ENTER key to know if your text is positive, negative, or neutral sentiment!
# """)
# # Add image
# image = st.image("sentiment.jpeg", width=400)
# # Get user input
# text = st.text_input("Type here:")
# # Define the CSS style for the app
# st.markdown(
# """
# <style>
# body {
# background-color: #f5f5f5;
# }
# h1 {
# color: #4e79a7;
# }
# </style>
# """,
# unsafe_allow_html=True
# )
# # Show sentiment output
# if text:
# sentiment, score = predict_sentiment(text)
# if sentiment == "Positive":
# st.success(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")
# elif sentiment == "Negative":
# st.error(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")
# else:
# st.warning(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")
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