app.py

import os
import streamlit as st
from st_aggrid import AgGrid
import pandas as pd
from transformers import pipeline, T5ForConditionalGeneration, T5Tokenizer
import matplotlib.pyplot as plt

# Set the page layout for Streamlit
st.set_page_config(layout="wide")

# 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")

# Title and Introduction
st.title("Table Question Answering and Data Analysis App")
st.markdown(""" 
    This app allows you to upload a table (CSV or Excel) and ask questions about the data.
    Based on your question, it will provide the corresponding answer using the **TAPAS** model and additional data processing.

    ### Available Features:
    - **mean()**: For "average", it computes the mean of the entire numeric DataFrame.
    - **sum()**: For "sum", it calculates the sum of all numeric values in the DataFrame.
    - **max()**: For "max", it computes the maximum value in the DataFrame.
    - **min()**: For "min", it computes the minimum value in the DataFrame.
    - **count()**: For "count", it counts the non-null values in the entire DataFrame.
""")

# 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),
                fit_columns_on_grid_load=True,  # Correct parameter to fit columns on grid load
                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:
                # Process TAPAS-related questions
                raw_answer = tqa(table=df, query=question, truncation=True)

                # Display raw answer from TAPAS on the screen
                st.markdown("<p style='font-family:sans-serif;font-size: 1rem;'>Raw TAPAS Answer: </p>", unsafe_allow_html=True)
                st.write(raw_answer)  # Display the raw TAPAS output

                # Extract relevant values for Plotly
                answer = raw_answer.get('answer', '')
                coordinates = raw_answer.get('coordinates', [])
                cells = raw_answer.get('cells', [])

                st.markdown("<p style='font-family:sans-serif;font-size: 1rem;'>Relevant Data for Plotly: </p>", unsafe_allow_html=True)
                st.write(f"Answer: {answer}")
                st.write(f"Coordinates: {coordinates}")
                st.write(f"Cells: {cells}")

                # If cells are returned, we will extract the corresponding values for plotting
                if cells:
                    # Convert cell values from strings to floats for plotting
                    cell_values = [float(cell) for cell in cells if cell.replace('.', '', 1).isdigit()]

                    # Plot the data if we have valid numeric values
                    if len(cell_values) > 0:
                        # Assuming that the coordinates or answer provides context on column names
                        # You can adjust the labels or data based on the actual output
                        column_names = [f"Row {i+1}" for i in range(len(cell_values))]

                        fig, ax = plt.subplots()
                        ax.bar(column_names, cell_values)
                        ax.set_xlabel('Rows')
                        ax.set_ylabel('Values')
                        ax.set_title('Bar Plot of TAPAS Answer')

                        # Display the plot in the Streamlit app
                        st.pyplot(fig)

            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.")