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# # 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}%!")
# 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")
# # 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 Neutral ...
# 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
# Load model and tokenizer
model = transformers.AutoModelForSequenceClassification.from_pretrained("DeeeTeeee01/mytest_trainer_roberta-base")
tokenizer = transformers.AutoTokenizer.from_pretrained("DeeeTeeee01/mytest_trainer_roberta-base")
@st.cache_resource
def predict_sentiment(text):
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
# # Get full model outputs
# outputs = model(text)
# Extract probabilities
negative = outputs[0][0]
positive = outputs[0][1]
neutral = outputs[0][2]
return negative, positive, neutral
# Page config
st.set_page_config(page_title="Sentiment Analysis", page_icon=":smile:")
# Title and intro text
st.header("Predict Text Sentiment")
st.write("Enter text below to classify its sentiment as Positive, Negative or Neutral")
# Input text
text = st.text_input("Enter text:")
# Predict button
predict_button = st.button("Predict")
# Prediction output
if predict_button and text:
# Get probabilities
negative, positive, neutral = predict_sentiment(text)
# Display probabilities
st.metric("Negative", f"{negative*100:.2f}%")
st.metric("Positive", f"{positive*100:.2f}%")
st.metric("Neutral", f"{neutral*100:.2f}%")
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