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import streamlit as st | |
import pandas as pd | |
from emotion_analysis import get_emotion | |
import base64 | |
def read_data(file_path): | |
file_extension = file_path.split('.')[-1].lower() | |
if file_extension == 'xlsx' or file_extension == 'xls': | |
data = pd.read_excel(file_path) | |
elif file_extension == 'csv': | |
data = pd.read_csv(file_path) | |
else: | |
raise ValueError("Unsupported file format. Only Excel (xlsx, xls) and CSV (csv) files are supported.") | |
return data | |
# Streamlit app | |
def main(): | |
st.title("Text Emotion Detection") | |
menu = ["Input Text", "Batch Processing"] | |
option = st.sidebar.radio("Select an option", menu) | |
if option == "Input Text": | |
text = st.text_area("Enter your text:") | |
if st.button("Submit"): | |
if text.strip() != "": | |
emotion_detail, confidence_score = get_emotion(text) | |
st.write("Detected Emotion") | |
st.write(f"{emotion_detail} - {confidence_score}") | |
else: | |
st.write("Please enter some text.") | |
elif option == "Batch Processing": | |
uploaded_file = st.file_uploader("Upload CSV or Excel file", type=["csv", "xlsx"]) | |
if uploaded_file is not None: | |
file_name = uploaded_file.name | |
file_extension = file_name.split('.')[-1].lower() | |
file_name = uploaded_file.name | |
if file_extension == 'xlsx' or file_extension == 'xls': | |
dataframe = pd.read_excel(uploaded_file) | |
elif file_extension == 'csv': | |
dataframe = pd.read_csv(uploaded_file) | |
else: | |
raise ValueError("Unsupported file format. Only Excel (xlsx, xls) and CSV (csv) files are supported.") | |
# dataframe = pd.read_excel(uploaded_file) | |
if "text" not in dataframe.columns: | |
st.write("CSV file should have a 'text' column.") | |
else: | |
dataframe["emotion"], dataframe["confidence"] = zip(*dataframe["text"].map(get_emotion)) | |
st.write("Detected Emotions") | |
st.write(dataframe) | |
# Download button | |
csv = dataframe.to_csv(index=False) | |
b64 = base64.b64encode(csv.encode()).decode() # Convert DataFrame to CSV string | |
href = f'<a href="data:file/csv;base64,{b64}" download="processed_data.csv">Download</a>' | |
st.markdown(href, unsafe_allow_html=True) | |
else: | |
pass | |
if __name__ == '__main__': | |
main() | |