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| import streamlit as st | |
| import pandas as pd | |
| from transformers import pipeline | |
| # Set the page layout for Streamlit | |
| st.set_page_config(layout="wide") | |
| # Initialize TAPAS pipeline for table-based question answering (multilingual) | |
| tqa = pipeline(task="table-question-answering", | |
| model="google/tapas-large-finetuned-wtq", | |
| device=0) # Assuming GPU is available, otherwise set device="cpu" | |
| # Title and Introduction | |
| st.title("Data Table with TAPAS NLP Integration") | |
| st.markdown(""" | |
| This app allows you to upload a table (CSV or Excel) and ask questions to extract information from the data. | |
| Using **TAPAS**, the app can interpret your questions and provide the corresponding answers. | |
| ### Available Features: | |
| - **Table Question Answering**: Ask questions related to the uploaded table. | |
| Upload your data and ask questions to extract answers. | |
| """) | |
| # File uploader in the sidebar | |
| file_name = st.sidebar.file_uploader("Upload file:", type=['csv', 'xlsx']) | |
| # File processing and question answering | |
| if file_name is None: | |
| st.markdown('<p class="font">Please upload an excel or csv file </p>', unsafe_allow_html=True) | |
| else: | |
| try: | |
| # Check file type and handle reading accordingly | |
| if file_name.name.endswith('.csv'): | |
| df = pd.read_csv(file_name, sep=';', encoding='ISO-8859-1') # Adjust encoding if needed | |
| elif file_name.name.endswith('.xlsx'): | |
| df = pd.read_excel(file_name, engine='openpyxl') # Use openpyxl to read .xlsx files | |
| else: | |
| st.error("Unsupported file type") | |
| df = None | |
| if df is not None: | |
| # Convert object columns to numeric where possible | |
| df = df.apply(pd.to_numeric, errors='ignore') | |
| st.write("Original Data:") | |
| st.write(df) | |
| # Display a sample of data for user reference | |
| st.write("Sample data:") | |
| st.write(df.head()) | |
| except Exception as e: | |
| st.error(f"Error reading file: {str(e)}") | |
| # User input for the question | |
| question = st.text_input(f'Ask your question related to the table') | |
| with st.spinner(): | |
| if st.button('Get Answer'): | |
| try: | |
| # Ensure the question is a valid string | |
| if not question or not isinstance(question, str): | |
| st.error("Please enter a valid question.") | |
| else: | |
| # Use TAPAS model to process the question | |
| result = tqa(table=df, query=question) | |
| # Display the raw output from TAPAS | |
| st.write("TAPAS Raw Output (Response):") | |
| st.write(result) # This will display the raw output from TAPAS | |
| # Optionally, you can output the raw output as plain text: | |
| st.text("Raw TAPAS Output (Plain Text):") | |
| st.text(str(result)) # This will display raw output as plain text | |
| # Check if TAPAS is returning the expected answer | |
| answer = result.get('answer', None) | |
| if answer: | |
| st.write(f"TAPAS Answer: {answer}") | |
| else: | |
| st.warning("TAPAS did not return a valid answer.") | |
| except Exception as e: | |
| st.warning(f"Error processing question or generating answer: {str(e)}") | |