app.py

import streamlit as st
from simpletransformers.classification import MultiLabelClassificationModel
import torch

# Function to make predictions
def predict(model, text):
    raw_outputs, _ = model.predict([text])
    return raw_outputs

# Streamlit App
def main():
    st.title("Dravidian-English Code Mixed TextSentiment Prediction App")

    # Language model selection
    selected_language = st.selectbox("Select Language Model", ["Kannada", "Malayalam", "Tamil"])

    # Load the pre-trained model based on the selected language
    model_paths = {
        "Kannada": "Diya-Roshan/xlm-code-mixed-kannada-sentiment-classifier",
        "Malayalam": "Diya-Roshan/xlm-code-mixed-malayalam-sentiment-classifier",  
        "Tamil": "Diya-Roshan/xlm-code-mixed-tamil-sentiment-classifier",  
    }

    if selected_language in model_paths:
        model_path = model_paths[selected_language]
        model = MultiLabelClassificationModel('xlm', model_path, use_cuda=False)

        # User input for text
        text_input = st.text_area("Enter text for prediction", "")

        # Make predictions when the user clicks the button
        if st.button("Predict"):
            if text_input:
                predictions = predict(model, text_input)

                # Display the predictions
                if predictions == [[1, 0, 0]]:
                    st.success('Positive Sentiment')
                elif predictions == [[0, 1, 0]]:
                    st.error('Negative Sentiment')
                else:
                    st.warning('Mixed Sentiment')

if __name__ == "__main__":
    main()