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
@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
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}%!")