Update app.py

app.py

@@ -3,36 +3,36 @@ import requests
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
+from st_aggrid import AgGrid
+from streamlit_echarts import st_echarts
 
-API_URL = "https://5415-34-127-95-74.ngrok-free.app/query"  
+# API endpoint for SQL Agent
+API_URL = "https://b6ae-35-234-51-197.ngrok-free.app/query"
 
-st.set_page_config(
-    page_title="SQL Agent with Streamlit", 
-    page_icon=":bar_chart:",  
-    layout="wide" 
-)
+st.set_page_config(page_title="SQL Agent with Streamlit", page_icon=":bar_chart:", layout="wide")
 
+# Sidebar Information
 with st.sidebar:
     st.write("## About Me")
     st.write("**Mahmoud Hassanen**") 
     st.write("**[LinkedIn Profile](https://www.linkedin.com/in/mahmoudhassanen99/)**")
 
-# Main content
+# Main Page
 st.title("SQL Agent with Streamlit")
 st.header("Analyze Sales Data with Natural Language Queries")
 
-# Input for the question
+# User Input
 question = st.text_input("Enter your question:")
 
+# Generate SQL Query
 if st.button("Generate SQL"):
     if question:
-        # API to generate SQL
         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.session_state.generated_sql = generated_sql  
             st.write("### Generated SQL Query:")
             st.code(generated_sql, language="sql")
         else:
@@ -40,27 +40,90 @@ if st.button("Generate SQL"):
     else:
         st.warning("Please enter a question.")
 
-# Allow the user to modify the SQL query
+# Modify and Execute 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
-            response = requests.post(API_URL, json={"sql_query": modified_sql})  # Send the modified SQL to the API
+            response = requests.post(API_URL, json={"sql_query": modified_sql})
             if response.status_code == 200:
                 data = response.json()
                 result_df = pd.read_json(data["result"], orient='records')
+
                 st.write("### Query Results:")
-                st.dataframe(result_df)
+                grid_response = AgGrid(result_df, height=250, fit_columns_on_grid_load=True)
+                selected_rows = grid_response['selected_rows']
+                df_selected = pd.DataFrame(selected_rows) if selected_rows else result_df 
+
+                # 🚀 Auto-Generate Charts
+                def auto_generate_chart(df):
+                    num_cols = df.select_dtypes(include=['number']).columns.tolist()
+                    cat_cols = df.select_dtypes(include=['object']).columns.tolist()
+
+                    if "date" in df.columns or "timestamp" in df.columns:
+                        options = {
+                            "title": {"text": "Time-Series Data"},
+                            "tooltip": {},
+                            "xAxis": {"type": "category", "data": df["date"].tolist()},
+                            "yAxis": {"type": "value"},
+                            "series": [{"name": "Value", "type": "line", "data": df[num_cols[0]].tolist()}],
+                        }
+                    elif len(cat_cols) > 0 and len(num_cols) == 1:
+                        options = {
+                            "title": {"text": "Bar Chart"},
+                            "tooltip": {},
+                            "xAxis": {"type": "category", "data": df[cat_cols[0]].tolist()},
+                            "yAxis": {"type": "value"},
+                            "series": [{"name": num_cols[0], "type": "bar", "data": df[num_cols[0]].tolist()}],
+                        }
+                    elif len(num_cols) == 2:
+                        options = {
+                            "title": {"text": "Scatter Plot"},
+                            "tooltip": {},
+                            "xAxis": {"type": "value"},
+                            "yAxis": {"type": "value"},
+                            "series": [{"name": "Data", "type": "scatter", "data": df[num_cols].values.tolist()}],
+                        }
+                    elif len(cat_cols) > 0 and len(num_cols) > 1:
+                        options = {
+                            "title": {"text": "Stacked Bar Chart"},
+                            "tooltip": {},
+                            "xAxis": {"type": "category", "data": df[cat_cols[0]].tolist()},
+                            "yAxis": {"type": "value"},
+                            "series": [{"name": col, "type": "bar", "stack": "stack", "data": df[col].tolist()} for col in num_cols],
+                        }
+                    elif len(num_cols) == 1:
+                        options = {
+                            "title": {"text": "Pie Chart"},
+                            "tooltip": {},
+                            "series": [{
+                                "name": num_cols[0],
+                                "type": "pie",
+                                "radius": "50%",
+                                "data": [{"name": cat, "value": val} for cat, val in zip(df[cat_cols[0]], df[num_cols[0]])],
+                            }],
+                        }
+                    else:
+                        return None
+                    
+                    return options
+
+                chart_options = auto_generate_chart(df_selected)
                 
-                # Visualize the data (if applicable)
+                if chart_options:
+                    st_echarts(options=chart_options, height="400px")
+                else:
+                    st.warning("No suitable chart found for this data.")
+
+                # Static Seaborn Visualization (Optional)
                 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_df, ax=ax)
                     st.pyplot(fig)
+
             else:
                 st.error(f"Error executing SQL: {response.json().get('error')}")
         except Exception as e:
-            st.error(f"Error executing SQL: {e}")
+            st.error(f"Error executing SQL: {e}")