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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:
# Convert object columns to numeric where possible
df = df.apply(pd.to_numeric, errors='ignore')
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 string
if not isinstance(question, str):
st.error("The question input is not a valid string.")
else:
# Use TAPAS model to process the question
result = tqa(table=df, query=question)
# Check if TAPAS is returning the expected answer
answer = result['answer']
st.write(f"TAPAS Answer: {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):
fig = px.scatter(df, x=columns[0], y=columns[1], 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.")
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:
fig = px.line(df, x=df.index, y=column, 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)}")
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