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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)}")
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