Update dcogsphere.py

dcogsphere.py

@@ -1,3 +1,14 @@
+import huggingface_hub
+import requests
+from gradio_client import Client
+client = Client("https://cogsphere-acogsphere.hf.space/")
+#client = Client("https://cognitivescience-acogspherea.hf.space/--replicas/k5l86/")
+#result = client.predict(
+#		fn_index=1
+#)
+#print(result)
+
+
 from python_actr import *
 log=log()
 
@@ -76,6 +87,13 @@ class RockPaperScissors(Model):
 
         self.trials+=1
         if self.trials>=100:
+            scora=self.score1.text
+            scorb=self.score2.text
+            result = client.predict(
+		scora,	
+		"played",	
+		scorb,
+		fn_index=0)
             log.score1=self.score1.text
             log.score2=self.score2.text
             self.stop()
@@ -92,12 +110,12 @@ class ProceduralPlayer(ACTR):
         choice.choose('paper')
     def play_scissors(goal='play rps',choice='waiting:True'):
         choice.choose('scissors')
-def logy():
-    env=RockPaperScissors()
-    env.model1=ProceduralPlayer()
-    env.model1.choice=env.choice1
-    env.model2=ProceduralPlayer()
-    env.model2.choice=env.choice2
-    response=env.run()
-    log_data = env.log
-    return log_data[0]['score1']
+#def logy():
+#    env=RockPaperScissors()
+#    env.model1=ProceduralPlayer()
+#    env.model1.choice=env.choice1
+#    env.model2=ProceduralPlayer()
+#    env.model2.choice=env.choice2
+#    response=env.run()
+#    log_data = env.log
+#    return log_data[0]['score1']