Acheived MVP

app.py

@@ -3,14 +3,29 @@ from my_chess.learner.environments import Chess
 
 from chessmodels import DCMinMax
 import streamlit as st
+from streamlit_image_coordinates import streamlit_image_coordinates
+
+class HvB(HumanVsBot):
+    @staticmethod
+    @st.cache_resource()
+    def get_hvb_manager(
+        _model,
+        _environment,
+        _extra_model_environment_context,
+        **kwargs):
+        return HvB(
+            model = _model,
+            environment = _environment,
+            extra_model_environment_context = _extra_model_environment_context,
+            **kwargs)
 
 def play(bot_select):
-    modelminmax = DCMinMax.from_pretrained("mzimm003/{}".format(bot_select))
-    play = HumanVsBot(
-        model=modelminmax,
-        environment=Chess(render_mode="rgb_array"),
-        extra_model_environment_context=lambda env: {"board":env.board}
+    play = HvB.get_hvb_manager(
+        _model=DCMinMax.from_pretrained("mzimm003/{}".format(bot_select)),
+        _environment=Chess(render_mode="rgb_array"),
+        _extra_model_environment_context=lambda env: {"board":env.board}
     )
+    st.session_state["board"] = streamlit_image_coordinates(play.render_board(), key="brd", click_and_drag=True)
     play.run()
 
 def main(kwargs=None):