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| import streamlit as st | |
| import pandas as pd | |
| import plotly.express as px | |
| from transformers import pipeline | |
| # Set the page layout for Streamlit | |
| st.set_page_config(layout="wide") | |
| # Initialize TAPAS pipeline for table-based question answering (multilingual) | |
| tqa = pipeline(task="table-question-answering", | |
| model="google/tapas-large-finetuned-wtq", | |
| device=0) # Assuming GPU is available, otherwise set device="cpu" | |
| # Title and Introduction | |
| st.title("Data Visualization App with TAPAS NLP Integration") | |
| st.markdown(""" | |
| This app allows you to upload a table (CSV or Excel) and ask questions to generate graphs visualizing the data. | |
| Using **TAPAS**, the app can interpret your questions and generate the corresponding graphs. | |
| ### Available Features: | |
| - **Scatter Plot**: Visualize relationships between two columns. | |
| - **Line Graph**: Visualize a single column over time. | |
| Upload your data and ask questions about the data to generate visualizations. | |
| """) | |
| # Language Selection | |
| language = st.selectbox( | |
| "Select the language of your question", | |
| ("English", "German", "French", "Spanish", "Italian", "Others") | |
| ) | |
| # 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: | |
| # Show the original data with text columns intact | |
| st.write("Original Data:") | |
| st.write(df) | |
| # Display a sample of data for graph generation | |
| st.write("Sample data for graph generation:") | |
| st.write(df.head()) | |
| except Exception as e: | |
| st.error(f"Error reading file: {str(e)}") | |
| # User input for the question | |
| question = st.text_input(f'Ask your graph-related question in {language}') | |
| with st.spinner(): | |
| if st.button('Generate Graph'): | |
| try: | |
| # Ensure the question is a valid string | |
| if not question or not isinstance(question, str): | |
| st.error("Please enter a valid question in the form of text.") | |
| else: | |
| # Use TAPAS model to process the question | |
| result = tqa(table=df, query=question) | |
| # Display the raw output from TAPAS | |
| st.write("TAPAS Raw Output (Response):") | |
| st.write(result) # This will display the raw output from TAPAS | |
| # Optionally, you can output the raw output as plain text: | |
| st.text("Raw TAPAS Output (Plain Text):") | |
| st.text(str(result)) # This will display raw output as plain text | |
| # Check if TAPAS is returning the expected answer | |
| answer = result.get('answer', None) | |
| if answer: | |
| st.write(f"TAPAS Answer: {answer}") | |
| else: | |
| st.warning("TAPAS did not return a valid answer.") | |
| # Determine if the question relates to graph generation | |
| if 'between' in question.lower() and 'and' in question.lower(): | |
| # This is a request for a scatter plot (two columns) | |
| 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): | |
| # Prepare the data for Plotly (scatter plot) | |
| x_data = df[columns[0]].dropna() # Extract x column, drop NaN values | |
| y_data = df[columns[1]].dropna() # Extract y column, drop NaN values | |
| # Ensure x_data and y_data have the same length | |
| min_length = min(len(x_data), len(y_data)) | |
| x_data = x_data[:min_length] | |
| y_data = y_data[:min_length] | |
| # Create the scatter plot | |
| fig = px.scatter(x=x_data, y=y_data, title=f"Scatter Plot between {columns[0]} and {columns[1]}") | |
| st.plotly_chart(fig, use_container_width=True) | |
| st.success(f"Here is the scatter plot between '{columns[0]}' and '{columns[1]}'.") | |
| else: | |
| st.warning("Columns not found in the dataset or the question format is incorrect.") | |
| elif 'column' in question.lower(): | |
| # This is a request for a line graph (single column) | |
| column = question.split('of')[-1].strip() # Handle 'of' keyword | |
| if column in df.columns: | |
| # Prepare the data for Plotly (line graph) | |
| column_data = df[column].dropna() # Drop NaN values | |
| # Create the line plot | |
| fig = px.line(x=column_data.index, y=column_data, title=f"Graph of column '{column}'") | |
| st.plotly_chart(fig, use_container_width=True) | |
| st.success(f"Here is the graph of column '{column}'.") | |
| else: | |
| st.warning(f"Column '{column}' not found in the data.") | |
| else: | |
| st.warning("Please ask a valid graph-related question (e.g., 'make a graph between column1 and column2').") | |
| except Exception as e: | |
| st.warning(f"Error processing question or generating graph: {str(e)}") | |