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import os
import streamlit as st
from st_aggrid import AgGrid
import pandas as pd
from transformers import pipeline, T5ForConditionalGeneration, T5Tokenizer
# Set the page layout for Streamlit
st.set_page_config(layout="wide")
# CSS styling
# ... (keep your existing CSS code)
# Initialize TAPAS pipeline
tqa = pipeline(task="table-question-answering",
model="google/tapas-large-finetuned-wtq",
device="cpu")
# Initialize T5 tokenizer and model for text generation
t5_tokenizer = T5Tokenizer.from_pretrained("t5-small")
t5_model = T5ForConditionalGeneration.from_pretrained("t5-small")
# 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:
numeric_columns = df.select_dtypes(include=['object']).columns
for col in numeric_columns:
df[col] = pd.to_numeric(df[col], errors='ignore')
st.write("Original Data:")
st.write(df)
df_numeric = df.copy()
df = df.astype(str)
# Display the first 5 rows of the dataframe in an editable grid
grid_response = AgGrid(
df.head(5),
columns_auto_size_mode='FIT_CONTENTS',
editable=True,
height=300,
width='100%',
)
except Exception as e:
st.error(f"Error reading file: {str(e)}")
# User input for the question
question = st.text_input('Type your question')
# Process the answer using TAPAS and T5
with st.spinner():
if st.button('Answer'):
try:
raw_answer = tqa(table=df, query=question, truncation=True)
st.markdown("<p style='font-family:sans-serif;font-size: 0.9rem;'> Raw Result From TAPAS: </p>",
unsafe_allow_html=True)
st.success(raw_answer)
answer = raw_answer['answer']
aggregator = raw_answer.get('aggregator', '')
coordinates = raw_answer.get('coordinates', [])
cells = raw_answer.get('cells', [])
if aggregator == 'SUM':
# Convert cell values to numbers and sum them
values = [float(cell) for cell in cells if cell.replace('.', '').isdigit()]
total_sum = sum(values)
base_sentence = f"The sum for '{question}' is {total_sum}."
else:
# Construct a base sentence for other aggregators or no aggregation
base_sentence = f"The answer from TAPAS for '{question}' is {answer}."
if coordinates and cells:
rows_info = [f"Row {coordinate[0] + 1}, Column '{df.columns[coordinate[1]]}' with value {cell}"
for coordinate, cell in zip(coordinates, cells)]
rows_description = " and ".join(rows_info)
base_sentence += f" This includes the following data: |