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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()
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