app.py

import pandas as pd
import streamlit as st
from transformers import TapasForQuestionAnswering, TapasTokenizer, T5ForConditionalGeneration, T5Tokenizer
import torch

# Assuming df is uploaded or pre-defined (you can replace with actual data loading logic)
# Example DataFrame (replace with your actual file or data)
data = {
    'Column1': [1, 2, 3, 4],
    'Column2': [5.5, 6.5, 7.5, 8.5],
    'Column3': ['a', 'b', 'c', 'd']
}
df = pd.DataFrame(data)

# Check if DataFrame is valid
if df is not None and not df.empty:
    # Select numeric columns
    df_numeric = df.select_dtypes(include='number')
else:
    df_numeric = pd.DataFrame()  # Empty DataFrame if input is invalid

# 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")

# 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.")


# Assuming 'column_name' exists and is selected or provided by the user
# Example of getting 'column_name' from user input (adjust this part according to your app):
column_name = st.selectbox("Select a column", df.columns)

# 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}"
""")