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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 | |
| 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.") | |