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import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
import streamlit as st
from st_aggrid import AgGrid
import pandas as pd
from transformers import pipeline, T5ForConditionalGeneration, T5Tokenizer
st.set_page_config(layout="wide")
style = '''
<style>
body {background-color: #F5F5F5; color: #000000;}
header {visibility: hidden;}
div.block-container {padding-top:4rem;}
section[data-testid="stSidebar"] div:first-child {
padding-top: 0;
}
.font {
text-align:center;
font-family:sans-serif;font-size: 1.25rem;}
</style>
'''
st.markdown(style, unsafe_allow_html=True)
st.markdown('<p style="font-family:sans-serif;font-size: 1.9rem;"> HertogAI Q&A table V1 using TAPAS and Text Generated</p>', unsafe_allow_html=True)
st.markdown("<p style='font-family:sans-serif;font-size: 0.9rem;'>Pre-trained TAPAS model runs on max 64 rows and 32 columns data. Make sure the file data doesn't exceed these dimensions.</p>", unsafe_allow_html=True)
# Initialize TAPAS and Hugging Face Model (T5 for NLP generation)
tqa = pipeline(task="table-question-answering",
model="google/tapas-large-finetuned-wtq",
device="cpu")
model_name = "t5-small" # You can use a larger model or GPT as needed
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
# Function to generate natural language from TAPAS output
def generate_nlp_from_tapas(tapas_output, df):
"""
Use Hugging Face's T5 model to generate natural language text from TAPAS output.
"""
try:
# Construct prompt using TAPAS output
answer = tapas_output['answer']
coordinates = tapas_output['coordinates']
answer_data = [df.iloc[row, col] for row, col in coordinates]
# Format the prompt for NLP model
prompt = f"Answer: {answer}. Data Location: Rows {coordinates}, Values: {answer_data}. Please summarize this information in a natural language sentence."
# Tokenize input and generate response
inputs = tokenizer.encode(prompt, return_tensors="pt", truncation=True, max_length=512)
outputs = model.generate(inputs, max_length=100, num_beams=5, early_stopping=True)
# Decode and return the generated response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
except Exception as e:
return f"Error generating response: {str(e)}"
file_name = st.sidebar.file_uploader("Upload file:", type=['csv', 'xlsx'])
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
# Continue with further processing if df is loaded
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)
# Create a copy for numerical operations
df_numeric = df.copy()
df = df.astype(str)
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)}")
question = st.text_input('Type your question')
with st.spinner():
if(st.button('Answer')):
try:
# Get the raw answer from TAPAS
raw_answer = tqa(table=df, query=question, truncation=True)
st.markdown("<p style='font-family:sans-serif;font-size: 0.9rem;'> Raw Result: </p>", unsafe_allow_html=True)
st.success(raw_answer)
# Use Hugging Face's T5 model to generate NLP text from TAPAS output
final_answer = generate_nlp_from_tapas(raw_answer, df)
# Display the generated answer in a simple format
st.markdown("<p style='font-family:sans-serif;font-size: 0.9rem;'> Generated Answer: </p>", unsafe_allow_html=True)
st.success(final_answer)
except Exception as e:
st.warning(f"Error: {str(e)} - Please retype your question and ensure it is correctly formatted.")
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