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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("Justin-J/finetuned_sentiment_modell")
model = AutoModelForSequenceClassification.from_pretrained("Justin-J/finetuned_sentiment_modell")
# 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("""
# 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 Tags
st.markdown(
"""
<div class="row">
<div class="column">
<img src="https://user-images.githubusercontent.com/115732734/271723332-6c824e95-5e2f-48ec-af1c-b66ac7db1d7a.jpeg" style="width:550"></div>
<div class="column">
<img src="https://user-images.githubusercontent.com/115732734/271723345-50f27ca9-94ee-4e7c-ad3b-2b10f27d31bb.jpeg" style="width:550"></div>
</div>
<style>
.row {
display: flex;
}
.column {
flex: 33.33%;
padding: 5px;
}
</style>
""",
unsafe_allow_html=True
)
# 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}%!") |