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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 plotly.express as px | |
| # 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. | |
| - **Graph Generation**: You can ask questions like "make a graph of column sales?" or "make a graph between sales and expenses?". The app will generate interactive graphs for you. | |
| Upload your data and ask questions to get both answers and visualizations. | |
| """) | |
| # 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') | |
| # Check if the question is about generating a graph | |
| is_graph_query = False | |
| if 'graph' in question.lower(): | |
| is_graph_query = True | |
| # Process the answer using TAPAS and T5 | |
| with st.spinner(): | |
| if st.button('Answer'): | |
| try: | |
| if not is_graph_query: | |
| # Process TAPAS-related questions if it's not a graph query | |
| 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 TAPAS is returning a list of numbers for "average" like you mentioned | |
| if "average" in question.lower() and cells: | |
| # Assuming cells are numeric values that can be plotted in a graph | |
| plot_data = [float(cell) for cell in cells] # Convert cells to numeric data | |
| # Create a DataFrame for Plotly | |
| plot_df = pd.DataFrame({ 'Index': list(range(1, len(plot_data) + 1)), 'Value': plot_data }) | |
| # Generate a graph using Plotly | |
| fig = px.line(plot_df, x='Index', y='Value', title=f"Graph for '{question}'") | |
| st.plotly_chart(fig, use_container_width=True) | |
| else: | |
| st.write(f"No data to plot for the question: '{question}'") | |
| else: | |
| # Handle graph-related questions | |
| if 'between' in question.lower() and 'and' in question.lower(): | |
| columns = question.split('between')[-1].split('and') | |
| columns = [col.strip() for col in columns] | |
| if len(columns) == 2 and all(col in df.columns for col in columns): | |
| fig = px.scatter(df, x=columns[0], y=columns[1], title=f"Graph between {columns[0]} and {columns[1]}") | |
| st.plotly_chart(fig, use_container_width=True) | |
| st.success(f"Here is the graph between '{columns[0]}' and '{columns[1]}'.") | |
| else: | |
| st.warning("Columns not found in the dataset.") | |
| elif 'column' in question.lower(): | |
| column = question.split('of')[-1].strip() | |
| if column in df.columns: | |
| fig = px.line(df, x=df.index, y=column, title=f"Graph of column '{column}'") | |
| st.plotly_chart(fig, use_container_width=True) | |
| st.stop() # This halts further execution | |
| 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.") | |