update to clickhouse python client

.gitignore

@@ -0,0 +1,2 @@
+.streamlit/
+__pycache__

app.py

@@ -9,7 +9,8 @@ import logging
 from os import environ
 from transformers import OwlViTProcessor, OwlViTForObjectDetection
 from bot import Bot, Message
-from myscaledb import Client
+from parse import parse
+from clickhouse_connect import get_client
 from classifier import Classifier, prompt2vec, tune, SplitLayer
 from query_model import simple_query, topk_obj_query, rev_query
 from card_model import card, obj_card, style
@@ -62,11 +63,10 @@ def init_db():
         client:     Database connection object
     """
     meta = []
-    client = Client(
-        url=st.secrets["DB_URL"], user=st.secrets["USER"], password=st.secrets["PASSWD"]
+    r = parse("{http_pre}://{host}:{port}", st.secrets["DB_URL"])
+    client = get_client(
+        host=r['host'], port=r['port'], user=st.secrets["USER"], password=st.secrets["PASSWD"]
     )
-    # We can check if the connection is alive
-    assert client.is_alive()
     return meta, client
 
 

classifier.py

@@ -114,7 +114,8 @@ class Classifier:
                         SELECT groupArray(arrayPopBack(prelogit)) AS X, 
                             groupArray(1/(1+exp(-arraySum(arrayMap((x,y)->x*y, prelogit, {xq_s[n]}))))) AS Y, {labels} AS GT
                         FROM {self.obj_db} WHERE obj_id IN {objs})"""
-                grad.append(torch.as_tensor(self.client.fetch(grad_q_str)[0]['grad']))
+                grad_ = [r['grad'] for r in self.client.query(grad_q_str).named_results()][0]
+                grad.append(torch.as_tensor(grad_))
             # update weights
             grad = torch.stack(grad, dim=0)
             self.weight -= 0.1 * grad
@@ -124,7 +125,6 @@ class Classifier:
         return xq
 
 
-
 class SplitLayer(torch.nn.Module):
     def forward(self, x):
         return torch.split(x, 1, dim=-1)

query_model.py

@@ -38,7 +38,7 @@ def topk_obj_query(client, xq, IMG_DB_NAME, OBJ_DB_NAME,
                     ({_subq_str})
             GROUP BY img_id, img_url, img_w, img_h ORDER BY img_score DESC
     """
-    xc = client.fetch(q_str)
+    xc = [{k: v for k, v in r.items()} for r in client.query(q_str).named_results()]
     return xc
 
 
@@ -74,7 +74,7 @@ def rev_query(client, xq, img_ids, IMG_DB_NAME, OBJ_DB_NAME, thresh=0.08):
                 ({_subq_str})
             GROUP BY img_id, img_url, img_w, img_h ORDER BY img_score DESC
     """
-    xc = client.fetch(q_str)
+    xc = [{k: v for k, v in r.items()} for r in client.query(q_str).named_results()]
     return xc
 
 
@@ -104,5 +104,5 @@ def simple_query(client, xq, IMG_DB_NAME, OBJ_DB_NAME, thresh=0.08, topk=10):
                 )
                 GROUP BY l
                 """
-    res = client.fetch(q_str)
-    return res
+    xc = [{k: v for k, v in r.items()} for r in client.query(q_str).named_results()]
+    return xc

requirements.txt

@@ -1,6 +1,7 @@
 transformers
 tqdm
-myscaledb-client==1.1.7
+clickhouse-connect
+parse
 streamlit
 numpy
 torch