app.py

import streamlit as st
import pandas as pd
from scipy.special import softmax
from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig

# Define the model path where the pre-trained model is saved on the Hugging Face model hub
model_path = "aaronayitey/Sentiment-classfication-ROBERTA-model"

# Initialize the tokenizer for the pre-trained model
tokenizer = AutoTokenizer.from_pretrained('roberta-base')

# Load the configuration for the pre-trained model
config = AutoConfig.from_pretrained(model_path)

# Load the pre-trained model
model = AutoModelForSequenceClassification.from_pretrained(model_path)

# Define a function to preprocess the text data
def preprocess(text):
    new_text = []
    # Replace user mentions with '@user'
    for t in text.split(" "):
        t = '@user' if t.startswith('@') and len(t) > 1 else t
        # Replace links with 'http'
        t = 'http' if t.startswith('http') else t
        new_text.append(t)
    # Join the preprocessed text
    return " ".join(new_text)

# Define a function to perform sentiment analysis on the input text
def sentiment_analysis(text):
    # Preprocess the input text
    text = preprocess(text)

    # Tokenize the input text using the pre-trained tokenizer
    encoded_input = tokenizer(text, return_tensors='pt')

    # Feed the tokenized input to the pre-trained model and obtain output
    output = model(**encoded_input)

    # Compute the softmax using torch
    scores = torch.softmax(output.logits, dim=1)[0].tolist()

    # Format the output dictionary with the predicted scores
    labels = ['Negative', 'Neutral', 'Positive']
    scores = {l: float(s) for (l, s) in zip(labels, scores)}

    # Get the label with the highest score
    max_score_label = max(scores, key=scores.get)

    # Return the label with the highest score
    return max_score_label


# Streamlit app title and description
st.title("Sentiment Analysis")
st.write("Enter a text, and we'll determine its sentiment!")

# Input text box for user input
user_input = st.text_area("Enter text here:")

# Button to analyze sentiment
if st.button("Analyze Sentiment"):
    if user_input:
        # Perform sentiment analysis on the user's input
        sentiment_label = sentiment_analysis(user_input)
        st.write(f"Sentiment: {sentiment_label}")
    else:
        st.warning("Please enter some text for sentiment analysis.")