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import pandas as pd
import streamlit as st
from transformers import TapasForQuestionAnswering, TapasTokenizer, T5ForConditionalGeneration, T5Tokenizer
import torch
# Load TAPAS model and tokenizer
tqa_model = TapasForQuestionAnswering.from_pretrained("google/tapas-large-finetuned-wtq")
tqa_tokenizer = TapasTokenizer.from_pretrained("google/tapas-large-finetuned-wtq")
# Load T5 model and tokenizer for rephrasing
t5_model = T5ForConditionalGeneration.from_pretrained("t5-small")
t5_tokenizer = T5Tokenizer.from_pretrained("t5-small")
# Assuming 'df' is the DataFrame you are using and has numeric columns
df_numeric = df.select_dtypes(include='number')
# 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:
# Get the raw answer from TAPAS
inputs = tqa_tokenizer(table=df, query=question, return_tensors="pt")
with torch.no_grad():
outputs = tqa_model(**inputs)
raw_answer = tqa_tokenizer.decode(outputs.logits.argmax(dim=-1), skip_special_tokens=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)
# Extract relevant information from the TAPAS result
answer = raw_answer
aggregator = "average" # Example aggregator, adjust based on raw_answer if needed
coordinates = [] # Example, adjust based on raw_answer
cells = [] # Example, adjust based on raw_answer
# Construct a base sentence replacing 'SUM' with the query term
base_sentence = f"The {question.lower()} of the selected data 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}"
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)
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("Please retype your question and make sure to use the column name and cell value correctly.")
# Manually fix the aggregator if it returns an incorrect one
if 'MEDIAN' in question.upper() and 'AVERAGE' in aggregator.upper():
aggregator = 'MEDIAN'
elif 'MIN' in question.upper() and 'AVERAGE' in aggregator.upper():
aggregator = 'MIN'
elif 'MAX' in question.upper() and 'AVERAGE' in aggregator.upper():
aggregator = 'MAX'
elif 'TOTAL' in question.upper() and 'SUM' in aggregator.upper():
aggregator = 'SUM'
# Use the corrected aggregator for further processing
summary_type = aggregator.lower()
# Check if `column_name` is valid before proceeding
if column_name and column_name in df_numeric.columns:
# Now, calculate the correct value using pandas based on the corrected aggregator
if summary_type == 'sum':
numeric_value = df_numeric[column_name].sum()
elif summary_type == 'max':
numeric_value = df_numeric[column_name].max()
elif summary_type == 'min':
numeric_value = df_numeric[column_name].min()
elif summary_type == 'average':
numeric_value = df_numeric[column_name].mean()
elif summary_type == 'count':
numeric_value = df_numeric[column_name].count()
elif summary_type == 'median':
numeric_value = df_numeric[column_name].median()
elif summary_type == 'std_dev':
numeric_value = df_numeric[column_name].std()
else:
numeric_value = answer # Fallback if something went wrong
else:
numeric_value = "Invalid column"
# Construct a natural language response
if summary_type == 'sum':
natural_language_answer = f"The total {column_name} is {numeric_value}."
elif summary_type == 'maximum':
natural_language_answer = f"The highest {column_name} is {numeric_value}."
elif summary_type == 'minimum':
natural_language_answer = f"The lowest {column_name} is {numeric_value}."
elif summary_type == 'average':
natural_language_answer = f"The average {column_name} is {numeric_value}."
elif summary_type == 'count':
natural_language_answer = f"The number of entries in {column_name} is {numeric_value}."
elif summary_type == 'median':
natural_language_answer = f"The median {column_name} is {numeric_value}."
elif summary_type == 'std_dev':
natural_language_answer = f"The standard deviation of {column_name} is {numeric_value}."
else:
natural_language_answer = f"The value for {column_name} is {numeric_value}."
# Display the result to the user
st.markdown("<p style='font-family:sans-serif;font-size: 0.9rem;'> Analysis Results: </p>", unsafe_allow_html=True)
st.success(f"""
• Answer: {natural_language_answer}
Data Location:
• Column: {column_name}
Additional Context:
• Query Asked: "{question}"
""")
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