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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', [])
# Handle aggregation based on user question or TAPAS output
if 'average' in question.lower() or aggregator == 'AVG':
avg_value = df_numeric.mean().mean() # Calculate average of the entire DataFrame
base_sentence = f"The average for '{question}' is {avg_value:.2f}."
elif 'sum' in question.lower() or aggregator == 'SUM':
total_sum = df_numeric.sum().sum() # Calculate sum of all numeric values
base_sentence = f"The sum for '{question}' is {total_sum:.2f}."
elif 'max' in question.lower() or aggregator == 'MAX':
max_value = df_numeric.max().max() # Calculate max value across DataFrame
base_sentence = f"The maximum value for '{question}' is {max_value:.2f}."
elif 'min' in question.lower() or aggregator == 'MIN':
min_value = df_numeric.min().min() # Calculate min value across DataFrame
base_sentence = f"The minimum value for '{question}' is {min_value:.2f}."
elif 'count' in question.lower() or aggregator == 'COUNT':
row_count = df_numeric.count().sum() # Count of all non-null values
base_sentence = f"The total count of non-null values for '{question}' is {row_count}."
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: {rows_description}."
# Generate a fluent response using the T5 model, rephrasing the base sentence
input_text = f"Given the question: '{question}', generate a more human-readable response: {base_sentence}"
# Tokenize the input and generate a fluent response using T5
inputs = t5_tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
summary_ids = t5_model.generate(inputs, max_length=150, num_beams=4, early_stopping=True)
# Decode the generated text
generated_text = t5_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
# Display the final generated response
st.markdown("<p style='font-family:sans-serif;font-size: 0.9rem;'> Final Generated Response with LLM: </p>", unsafe_allow_html=True)
st.success(generated_text)
except Exception as e:
st.warning(f"Error processing question or generating answer: {str(e)}")
st.warning("Please retype your question and make sure to use the column name and cell value correctly.")
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