app.py

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", model = "DeeeTeeee01/mytest_trainer_roberta-base", tokenizer= "DeeeTeeee01/mytest_trainer_roberta-base")

    
    # 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("""
# Twit Analyzer
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
# 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}%!")