updating files

latest_result_DAA_QS.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0e2e2d7549920281a3912bd08b526a3c8dd5fc122e5f00bb470ff0d0cbfffda1
-size 5404
+oid sha256:f2c756e265448cf0a398c622efa0c15fcb67fc6022259250767bcec8d513fda8
+size 5431

scripts/daa.py

@@ -101,18 +101,18 @@ def get_service_id_from_trader_address(trader_address: str, service_map: dict):
     return None
 
 
-def prepare_predict_agents_dataset():
+def prepare_predict_services_dataset():
     # Read the service map pickle file
     with open(ROOT_DIR / "service_map.pkl", "rb") as f:
         service_map = pickle.load(f)
     content = []
-    # Find all the safe addresses in the service map whose agent_id is equal to 25
+    # Find all the safe addresses in the service map whose agent_id is equal to 25 or 14
     for key, value in service_map.items():
         print(f"value = {value}")
         if "agent_id" not in value:
             print(f"agent_id not found in value {value}")
             continue
-        if value["agent_id"] == 25:
+        if value["agent_id"] == 25 or value["agent_id"] == 14:
             agent_dict = {}
             # label the predict agents into two categories: pearl and quickstart
             owner_address = value["owner_address"]
@@ -123,10 +123,10 @@ def prepare_predict_agents_dataset():
             )
             content.append(agent_dict)
     # build the dataframe from the list of dictionaries
-    predict_agents = pd.DataFrame(content)
-    print(f"Number of unique predict agents = {len(predict_agents)}")
+    predict_services = pd.DataFrame(content)
+    print(f"Number of unique predict agents = {len(predict_services)}")
     # save the dataset as a csv file
-    predict_agents.to_csv(ROOT_DIR / "olas_trader_agents.csv", index=False)
+    predict_services.to_csv(ROOT_DIR / "predict_services.csv", index=False)
 
 
 def setup_dune_python_client():
@@ -145,29 +145,40 @@ def setup_dune_python_client():
     return dune_client
 
 
-def load_olas_agents_dataset_in_dune(dune_client: DuneClient):
+def load_predict_services_file(dune_client: DuneClient):
     """Function to load the olas dataset in dune"""
 
     # Prepare the olas dataset
-    # prepare_predict_agents_dataset()
+    # prepare_predict_services_dataset()
     try:
-        with open(ROOT_DIR / "olas_trader_agents.csv", "r") as open_file:
+        with open(ROOT_DIR / "predict_services.csv", "r") as open_file:
             data = open_file.read()
 
         # Upload the CSV data
         print("loading the dataset in dune")
-        dune_client.create_table(
-            table_name="olas_trader_agents",
-            description="Olas trader agents found in Pearl and Quickstart markets",
-            schema=[
-                {"name": "date", "type": "timestamp"},
-                {"name": "dgs10", "type": "double", "nullable": True},
-            ],
+        # dune_client.create_table(
+        #     table_name="olas_trader_agents",
+        #     description="Olas trader agents found in Pearl and Quickstart markets",
+        #     schema=[
+        #         {"name": "date", "type": "timestamp"},
+        #         {"name": "dgs10", "type": "double", "nullable": True},
+        #     ],
+        #     is_private=False,
+        #     namespace="cyberosa",
+        # )
+        # use the dune client to upload the dataset predict_services.csv
+        dune_client.upload_csv(
+            table_name="predict_services",
+            data=data,
+            description="Olas predict services found at the service registry",
+            # schema=[
+            #     {"name": "safe_address", "type": "text"},
+            #     {"name": "service_id", "type": "text"},
+            #     {"name": "market_creator", "type": "text"},
+            # ],
             is_private=False,
-            namespace="cyberosa",
         )
-
-        print(f"Dataset uploaded successfully!")
+        print(f"CSV file uploaded successfully!")
 
     except FileNotFoundError:
         print(f"Error: CSV file not found at {ROOT_DIR / "olas_trader_agents.csv"}")
@@ -245,4 +256,6 @@ def prepare_daa_data():
 
 if __name__ == "__main__":
     prepare_daa_data()
-    # prepare_predict_agents_dataset()
+    # prepare_predict_services_dataset()
+    # dune = setup_dune_python_client()
+    # load_predict_services_file(dune_client=dune)