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| import streamlit as st | |
| import transformers | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| # Load the model and tokenizer | |
| tokenizer = AutoTokenizer.from_pretrained("ikoghoemmanuell/finetuned_sentiment_modell") | |
| model = AutoModelForSequenceClassification.from_pretrained("ikoghoemmanuell/finetuned_sentiment_modell") | |
| # Define the function for sentiment analysis | |
| 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(""" | |
| # How Positive or Negative is your Text? | |
| Enter some text and we'll tell you if it has a positive, negative, or neutral sentiment! | |
| """) | |
| # Add image | |
| image = st.image("https://i0.wp.com/thedatascientist.com/wp-content/uploads/2018/10/sentiment-analysis.png", width=400) | |
| # Get user input | |
| text = st.text_input("Enter some text 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}%!") |