DeeeTeeee01 commited on
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7cfa370
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  1. app.py +66 -0
  2. requirements.txt +2 -0
app.py ADDED
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+ import streamlit as st
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+ import transformers
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+ import torch
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+
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+ # Load the model and tokenizer
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+ model = transformers.AutoModelForSequenceClassification.from_pretrained("DeeeTeeee01/twitter-xlm-roberta-base-sentiment_dee")
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+ tokenizer = transformers.AutoTokenizer.from_pretrained("DeeeTeeee01/twitter-xlm-roberta-base-sentiment_dee")
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+
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+ # Define the function for sentiment analysis
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+ @st.cache_resource
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+ def predict_sentiment(text):
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+ # Load the pipeline.
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+ pipeline = transformers.pipeline("sentiment-analysis")
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+
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+ # Predict the sentiment.
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+ prediction = pipeline(text)
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+ sentiment = prediction[0]["label"]
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+ score = prediction[0]["score"]
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+
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+ return sentiment, score
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+
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+ # Setting the page configurations
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+ st.set_page_config(
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+ page_title="Sentiment Analysis App",
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+ page_icon=":smile:",
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+ layout="wide",
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+ initial_sidebar_state="auto",
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+ )
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+
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+ # Add description and title
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+ st.write("""
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+ # Predict if your text is Positive, Negative or Nuetral ...
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+ Please type your text and press ENTER key to know if your text is positive, negative, or neutral sentiment!
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+ """)
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+
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+
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+ # Add image
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+ image = st.image("https://medium.com/scrapehero/sentiment-analysis-using-svm-338d418e3ff1", width=400)
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+
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+ # Get user input
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+ text = st.text_input("Type here:")
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+
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+ # Define the CSS style for the app
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+ st.markdown(
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+ """
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+ <style>
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+ body {
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+ background-color: #f5f5f5;
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+ }
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+ h1 {
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+ color: #4e79a7;
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+ }
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+ </style>
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+ """,
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+ unsafe_allow_html=True
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+ )
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+
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+ # Show sentiment output
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+ if text:
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+ sentiment, score = predict_sentiment(text)
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+ if sentiment == "Positive":
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+ st.success(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")
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+ elif sentiment == "Negative":
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+ st.error(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")
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+ else:
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+ st.warning(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")
requirements.txt ADDED
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+ torch
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+ transformers