switch to llama llm 4 && git push

@tool

def sql_engine_tool(
query: str,
) -> str:
"""
Allows you to perform SQL queries on the table. Returns a string representation of the result.
The table is named receipts. Its description is as follows:
Columns:
- receipt_id: INTEGER
- customer_name: VARCHAR(16)
- price: FLOAT
- tip: FLOAT

Args:
query: The query to perform. This should be correct SQL.

"""
output = ""
with engine.connect() as con:
rows = con.execute(text(query))
for row in rows:
output += "\n" + str(row)
return output

app.py

@@ -47,6 +47,7 @@ def sql_engine_tool(
 
     """
     output = ""
+    print(engine)
     with engine.connect() as con:
         rows = con.execute(text(query))
         for row in rows:
@@ -94,6 +95,7 @@ def init_db(engine):
     insert_rows_into_table(rows, receipts)
     with engine.begin() as conn:
         print("SELECT test", conn.execute(text("SELECT * FROM receipts")).fetchall())
+    print(engine)
     return engine
 
 
@@ -101,7 +103,7 @@ if __name__ == "__main__":
     engine = create_engine("sqlite:///:memory:")
     engine = init_db(engine)
     model = HfApiModel(
-        model_id="",
+        model_id="meta-llama/Meta-Llama-3.1-8B-Instruct",
         token=os.getenv("my_first_agents_hf_tokens"),
     )