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code-mixed-text-sentiment-classification

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code-mixed-text-sentiment-classification / app.py

Update app.py

fb2acae verified almost 3 years ago

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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()

	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()