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# import streamlit as st
# import requests
# import json
# import pandas as pd
# st.set_page_config(page_title="QueryMate: Text to SQL & CSV")
# st.markdown("# QueryMate: Text to SQL & CSV 💬🗄️")
# st.markdown('''Welcome to QueryMate, your friendly assistant for converting natural language queries into SQL statements and CSV outputs!
# Let's get started with your data queries!''')
# # Load chat history
# def load_chat_history():
# try:
# with open('chat_history.json', 'r') as f:
# return json.load(f)
# except FileNotFoundError:
# return []
# def save_chat_history(history):
# with open('chat_history.json', 'w') as f:
# json.dump(history, f)
# chat_history = load_chat_history()
# # Data source selection
# data_source = st.radio("Select Data Source:", ('SQL Database', 'Employee CSV'))
# # Predefined queries
# predefined_queries = {
# 'SQL Database': [
# 'Print all students',
# 'Count total number of students',
# 'List students in Data Science class'
# ],
# 'Employee CSV': [
# 'Print employees having the department id equal to 100',
# 'Count total number of employees',
# 'List Top 5 employees according to salary in descending order'
# ]
# }
# st.markdown(f"### Predefined Queries for {data_source}")
# # Create buttons for predefined queries
# for query in predefined_queries[data_source]:
# if st.button(query):
# st.session_state.predefined_query = query
# st.markdown("### Enter Your Question")
# question = st.text_input("Input: ", key="input", value=st.session_state.get('predefined_query', ''))
# # Submit button
# submit = st.button("Submit")
# if submit:
# # Send request to FastAPI backend
# response = requests.post("http://localhost:8000/query",
# json={"question": question, "data_source": data_source})
# if response.status_code == 200:
# data = response.json()
# st.markdown(f"## Generated {'SQL' if data_source == 'SQL Database' else 'Pandas'} Query")
# st.code(data['query'])
# st.markdown("## Query Results")
# result = data['result']
# if isinstance(result, list) and len(result) > 0:
# if isinstance(result[0], dict):
# # For CSV queries that return a list of dictionaries
# df = pd.DataFrame(result)
# st.dataframe(df)
# elif isinstance(result[0], list):
# # For SQL queries that return a list of lists
# df = pd.DataFrame(result)
# st.dataframe(df)
# else:
# # For single column results
# st.dataframe(pd.DataFrame(result, columns=['Result']))
# elif isinstance(result, dict):
# # For single row results
# st.table(result)
# else:
# # For scalar results or empty results
# st.write(result)
# if data_source == 'Employee CSV':
# st.markdown("## Available CSV Columns")
# st.write(data['columns'])
# # Update chat history
# chat_history.append(f"👨💻({data_source}): {question}")
# chat_history.append(f"🤖: {data['query']}")
# save_chat_history(chat_history)
# else:
# st.error(f"Error processing your request: {response.text}")
# # Clear the predefined query from session state
# st.session_state.pop('predefined_query', None)
# # Display chat history
# st.markdown("## Chat History")
# for message in chat_history:
# st.text(message)
# # Option to clear chat history
# if st.button("Clear Chat History"):
# chat_history.clear()
# save_chat_history(chat_history)
# st.success("Chat history cleared!")
import streamlit as st
import requests
import pandas as pd
st.set_page_config(page_title="QueryMate: Text to SQL & CSV")
st.markdown("# QueryMate: Text to SQL & CSV 💬🗄️")
st.markdown('''Welcome to QueryMate, your friendly assistant for converting natural language queries into SQL statements and CSV outputs!
Let's get started with your data queries!''')
# Initialize chat history in session state if it doesn't exist
if 'chat_history' not in st.session_state:
st.session_state.chat_history = []
# Data source selection
data_source = st.radio("Select Data Source:", ('SQL Database', 'Employee CSV'))
# Predefined queries
predefined_queries = {
'SQL Database': [
'Print all students',
'Count total number of students',
'List students in Data Science class'
],
'Employee CSV': [
'Print employees having the department id equal to 100',
'Count total number of employees',
'List Top 5 employees according to salary in descending order'
]
}
st.markdown(f"### Predefined Queries for {data_source}")
# Create buttons for predefined queries
for query in predefined_queries[data_source]:
if st.button(query):
st.session_state.predefined_query = query
st.markdown("### Enter Your Question")
question = st.text_input("Input: ", key="input", value=st.session_state.get('predefined_query', ''))
# Submit button
submit = st.button("Submit")
if submit:
# Send request to FastAPI backend
response = requests.post("http://localhost:8000/query",
json={"question": question, "data_source": data_source})
if response.status_code == 200:
data = response.json()
st.markdown(f"## Generated {'SQL' if data_source == 'SQL Database' else 'Pandas'} Query")
st.code(data['query'])
st.markdown("## Query Results")
result = data['result']
if isinstance(result, list) and len(result) > 0:
if isinstance(result[0], dict):
# For CSV queries that return a list of dictionaries
df = pd.DataFrame(result)
st.dataframe(df)
elif isinstance(result[0], list):
# For SQL queries that return a list of lists
df = pd.DataFrame(result)
st.dataframe(df)
else:
# For single column results
st.dataframe(pd.DataFrame(result, columns=['Result']))
elif isinstance(result, dict):
# For single row results
st.table(result)
else:
# For scalar results or empty results
st.write(result)
if data_source == 'Employee CSV':
st.markdown("## Available CSV Columns")
st.write(data['columns'])
# Update chat history in session state
st.session_state.chat_history.append(f"👨💻({data_source}): {question}")
st.session_state.chat_history.append(f"🤖: {data['query']}")
else:
st.error(f"Error processing your request: {response.text}")
# Clear the predefined query from session state
st.session_state.pop('predefined_query', None)
# Display chat history
st.markdown("## Chat History")
for message in st.session_state.chat_history:
st.text(message)
# Option to clear chat history
if st.button("Clear Chat History"):
st.session_state.chat_history = []
st.success("Chat history cleared!") |