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import streamlit as st
import requests
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
st.set_page_config(
page_title="SQL Agent with Streamlit",
page_icon=":bar_chart:",
layout="wide"
)
with st.sidebar:
st.write("## About Me")
st.write("**Mahmoud Hassanen**")
st.write("**[LinkedIn Profile](https://www.linkedin.com/in/mahmoudhassanen99//)**")
st.title("SQL Agent with Streamlit")
st.header("Analyze Sales Data with Natural Language Queries")
API_URL = "https://14d0-34-27-134-153.ngrok-free.app/query"
question = st.text_input("Enter your question:")
if st.button("Generate SQL"):
if question:
response = requests.post(API_URL, json={"question": question})
if response.status_code == 200:
data = response.json()
generated_sql = data["sql_query"]
st.session_state.generated_sql = generated_sql # Store the generated SQL in session state
st.write("### Generated SQL Query:")
st.code(generated_sql, language="sql")
else:
st.error(f"API Error: Status Code {response.status_code}")
else:
st.warning("Please enter a question.")
# Allow the user to modify the SQL query
if "generated_sql" in st.session_state:
modified_sql = st.text_area("Modify the SQL query (if needed):", st.session_state.generated_sql, height=200)
if st.button("Execute Modified Query"):
try:
# Execute the modified SQL query
result = execute_sql(modified_sql) # Use your existing execute_sql function
st.write("### Query Results:")
st.dataframe(result)
# Visualize the data (if applicable)
if 'region' in result.columns and 'total_sales' in result.columns:
st.write("### Total Sales by Region")
fig, ax = plt.subplots()
sns.barplot(x='region', y='total_sales', data=result, ax=ax)
st.pyplot(fig)
except Exception as e:
st.error(f"Error executing SQL: {e}")
# Function to execute SQL and return results
def execute_sql(sql_query):
# Create a SQLAlchemy connection string
connection_string = f"mssql+pyodbc://{username}:{password}@{server}/{database}?driver={driver.replace(' ', '+')}"
engine = create_engine(connection_string)
# Execute the query and fetch results
df = pd.read_sql(sql_query, engine)
return df |