Update app.py

app.py

@@ -1,49 +1,74 @@
-# Streamlit app code
 import streamlit as st
 import requests
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
 
-API_URL = "https://14d0-34-27-134-153.ngrok-free.app/query"  # Replace with your ngrok public URL
-
+# Set page configuration
 st.set_page_config(
-    page_title="SQL Agent with Streamlit", 
-    page_icon=":bar_chart:",  
-    layout="wide" 
+    page_title="SQL Agent with Streamlit",  # Page title
+    page_icon=":bar_chart:",  # Favicon emoji
+    layout="wide"  # Page layout option
 )
 
-with st.sidebar:
-    st.write("## About Me")
-    st.write("**Mahmoud Hassanen**") 
-    st.write("**[LinkedIn Profile](https://www.linkedin.com/in/mahmoudhassanen99//)**")
-    
+# Add a header and page name
 st.title("SQL Agent with Streamlit")
 st.header("Analyze Sales Data with Natural Language Queries")
 
+# Add a sidebar with your name and LinkedIn profile
+with st.sidebar:
+    st.write("## About Me")
+    st.write("**Name:** Your Name")  # Replace with your name
+    st.write("**LinkedIn:** [Your LinkedIn Profile](https://www.linkedin.com/in/your-profile/)")  # Replace with your LinkedIn URL
+
+# API URL (replace with your ngrok URL)
+API_URL = "https://c6d9-34-27-134-153.ngrok-free.app/query"  # Replace with your ngrok public URL
+
 # Input for the question
 question = st.text_input("Enter your question:")
 
-if st.button("Submit"):
+if st.button("Generate SQL"):
     if question:
+        # Call the API to generate SQL
         response = requests.post(API_URL, json={"question": question})
         
         if response.status_code == 200:
             data = response.json()
-            st.write("Generated SQL Query:")
-            st.code(data["sql_query"], language="sql")
-            
-            st.write("Query Results:")
-            result_df = pd.read_json(data["result"], orient='records')
-            st.dataframe(result_df)
+            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 'region' in result_df.columns and 'total_sales' in result_df.columns:
-                st.write("Total Sales by Region")
+            # 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_df, ax=ax)
+                sns.barplot(x='region', y='total_sales', data=result, ax=ax)
                 st.pyplot(fig)
-        else:
-            st.error(f"Error: {response.json().get('error')}")
-    else:
-        st.warning("Please enter a question.")
+        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