Update app.py

app.py

@@ -4,6 +4,8 @@ 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
+
 st.set_page_config(
     page_title="SQL Agent with Streamlit", 
     page_icon=":bar_chart:",  
@@ -18,51 +20,29 @@ with st.sidebar:
 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" 
-
+# Input for the question
 question = st.text_input("Enter your question:")
 
-if st.button("Generate SQL"):
+if st.button("Submit"):
     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)
+            st.write("Generated SQL Query:")
+            st.code(data["sql_query"], language="sql")
             
-            # Visualize the data (if applicable)
-            if 'region' in result.columns and 'total_sales' in result.columns:
-                st.write("### Total Sales by Region")
+            st.write("Query Results:")
+            result_df = pd.read_json(data["result"], orient='records')
+            st.dataframe(result_df)
+            
+            # Visualize the data
+            if 'region' in result_df.columns and 'total_sales' in result_df.columns:
+                st.write("Total Sales by Region")
                 fig, ax = plt.subplots()
-                sns.barplot(x='region', y='total_sales', data=result, ax=ax)
+                sns.barplot(x='region', y='total_sales', data=result_df, 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
+        else:
+            st.error(f"Error: {response.json().get('error')}")
+    else:
+        st.warning("Please enter a question.")