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Browse files- app.py +4 -0
- nim_gpt_functions.py +1 -1
app.py
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@@ -1,4 +1,5 @@
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import gradio as gr
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from nim_game_env import NimGameEnv
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from nim_gpt_functions import plan_move, execute_move
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@@ -55,6 +56,9 @@ def send_chat_msg(inp, chat_history, nim_game_env, temperature, openai_api_key):
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text_obs, observation, reward, done, info = execute_move(output, nim_game_env, openai_api_key)
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ascii_art = generate_game_state_ascii_art(observation, done, reward, AI_STR)
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chat_history.append((HUMAN_STR + ": " + inp, AI_STR + ": " + output))
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return chat_history, chat_history, ascii_art
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import gradio as gr
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import datetime
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from nim_game_env import NimGameEnv
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from nim_gpt_functions import plan_move, execute_move
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text_obs, observation, reward, done, info = execute_move(output, nim_game_env, openai_api_key)
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ascii_art = generate_game_state_ascii_art(observation, done, reward, AI_STR)
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print("\n==== date/time: " + str(datetime.datetime.now() - datetime.timedelta(hours=5)) + " ====")
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print("inp: " + inp, ", output: ", output, ", observation: ", observation)
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chat_history.append((HUMAN_STR + ": " + inp, AI_STR + ": " + output))
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return chat_history, chat_history, ascii_art
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nim_gpt_functions.py
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@@ -53,7 +53,7 @@ def plan_move(text_game_state, temperature, api_key):
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llm = OpenAI(model_name='text-davinci-003', temperature=temperature, max_tokens=100,
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openai_api_key=api_key)
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llm_chain = LLMChain(llm=llm, prompt=PLAN_MOVE_PROMPT_FROM_STRING_EXAMPLES, verbose=False)
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planned_move = llm_chain.run({'text_game_state': text_game_state})
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return planned_move
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llm = OpenAI(model_name='text-davinci-003', temperature=temperature, max_tokens=100,
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openai_api_key=api_key)
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llm_chain = LLMChain(llm=llm, prompt=PLAN_MOVE_PROMPT_FROM_STRING_EXAMPLES, verbose=False)
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planned_move = llm_chain.run({'text_game_state': text_game_state}).strip()
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return planned_move
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