Create app.py

app.py

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+import gradio as gr
+from sqlalchemy import create_engine, MetaData, Table, Column, String, Integer, Float, insert, text, inspect
+from smolagents import tool, CodeAgent, InferenceClientModel
+
+# --- Setup SQLite database (in-memory) ---
+engine = create_engine("sqlite:///:memory:")
+metadata_obj = MetaData()
+
+# Create receipts table
+receipts = Table(
+    "receipts",
+    metadata_obj,
+    Column("receipt_id", Integer, primary_key=True),
+    Column("customer_name", String(16), primary_key=True),
+    Column("price", Float),
+    Column("tip", Float),
+)
+metadata_obj.create_all(engine)
+
+# Insert sample data
+rows = [
+    {"receipt_id": 1, "customer_name": "Alan Payne", "price": 12.06, "tip": 1.20},
+    {"receipt_id": 2, "customer_name": "Alex Mason", "price": 23.86, "tip": 0.24},
+    {"receipt_id": 3, "customer_name": "Woodrow Wilson", "price": 53.43, "tip": 5.43},
+    {"receipt_id": 4, "customer_name": "Margaret James", "price": 21.11, "tip": 1.00},
+]
+for row in rows:
+    stmt = insert(receipts).values(**row)
+    with engine.begin() as conn:
+        conn.execute(stmt)
+
+# Create waiters table
+waiters = Table(
+    "waiters",
+    metadata_obj,
+    Column("receipt_id", Integer, primary_key=True),
+    Column("waiter_name", String(16), primary_key=True),
+)
+metadata_obj.create_all(engine)
+
+rows = [
+    {"receipt_id": 1, "waiter_name": "Corey Johnson"},
+    {"receipt_id": 2, "waiter_name": "Michael Watts"},
+    {"receipt_id": 3, "waiter_name": "Michael Watts"},
+    {"receipt_id": 4, "waiter_name": "Margaret James"},
+]
+for row in rows:
+    stmt = insert(waiters).values(**row)
+    with engine.begin() as conn:
+        conn.execute(stmt)
+
+# --- Define SQL tool for the agent ---
+@tool
+def sql_engine(query: str) -> str:
+    """
+    Executes SQL queries on the available tables: receipts and waiters.
+
+    Args:
+        query: SQL query string.
+    """
+    try:
+        output = ""
+        with engine.connect() as con:
+            rows = con.execute(text(query))
+            for row in rows:
+                output += "\n" + str(row)
+        return output or "No results."
+    except Exception as e:
+        return f"⚠️ SQL Error: {str(e)}"
+
+# Dynamically describe tables
+updated_description = "Allows SQL queries on the following tables:\n"
+inspector = inspect(engine)
+for table in ["receipts", "waiters"]:
+    columns_info = [(col["name"], col["type"]) for col in inspector.get_columns(table)]
+    table_description = f"\n\nTable '{table}':\nColumns:\n" + "\n".join(
+        [f"  - {name}: {col_type}" for name, col_type in columns_info]
+    )
+    updated_description += table_description
+sql_engine.description = updated_description
+
+# --- Create the agent ---
+agent = CodeAgent(
+    tools=[sql_engine],
+    model=InferenceClientModel("meta-llama/Meta-Llama-3-8B-Instruct"),
+)
+
+# --- Define Gradio interface ---
+def ask_agent(question):
+    """Ask the AI agent a question."""
+    result = agent.run(question)
+    return result
+
+demo = gr.Interface(
+    fn=ask_agent,
+    inputs=gr.Textbox(label="Ask a question (e.g., Who got the biggest tip?)"),
+    outputs=gr.Textbox(label="Agent response"),
+    title="🧠 Text-to-SQL Agent",
+    description="Ask natural-language questions about a restaurant receipts database. Powered by smolagents + Meta-Llama-3.",
+)
+
+demo.launch()