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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() | |