test

app.py

@@ -1,10 +1,36 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 import os
+from smolagents import tool, CodeAgent, HfApiModel, GradioUI  # type: ignore
+
+# testing teste
 """
 For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
 """
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta",token=os.getenv("my_first_agents_hf_tokens"))
+client = InferenceClient(
+    "HuggingFaceH4/zephyr-7b-beta", token=os.getenv("my_first_agents_hf_tokens")
+)
+
+
+def sql_engine(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
 
 
 def respond(
@@ -27,7 +53,9 @@ def respond(
 
     response = ""
 
-    for message in client.chat_completion(
+    # agent.run("Can you give me the name of the client who got the most expensive receipt?")
+
+    for message in agent.chat_completion(
         messages,
         max_tokens=max_tokens,
         stream=True,
@@ -58,9 +86,15 @@ demo = gr.ChatInterface(
         ),
     ],
 )
-
-
 if __name__ == "__main__":
+    agent = CodeAgent(
+        tools=[sql_engine],
+        model=HfApiModel(
+            model_id="meta-llama/Meta-Llama-3.1-8B-Instruct",
+            token=os.getenv("my_first_agents_hf_tokens"),
+        ),
+    )
+
     from sqlalchemy import (
         create_engine,
         MetaData,
@@ -73,16 +107,16 @@ if __name__ == "__main__":
         inspect,
         text,
     )
-    
+
     engine = create_engine("sqlite:///:memory:")
     metadata_obj = MetaData()
-    
+
     def insert_rows_into_table(rows, table, engine=engine):
         for row in rows:
             stmt = insert(table).values(**row)
             with engine.begin() as connection:
                 connection.execute(stmt)
-    
+
     table_name = "receipts"
     receipts = Table(
         table_name,
@@ -93,12 +127,22 @@ if __name__ == "__main__":
         Column("tip", Float),
     )
     metadata_obj.create_all(engine)
-    
+
     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},
+        {
+            "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,
+        },
     ]
     insert_rows_into_table(rows, receipts)
-    demo.launch()
+    GradioUI(agent).launch()