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d753c71
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1 Parent(s): 19ac70e

Update app.py

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Files changed (1) hide show
  1. app.py +35 -2
app.py CHANGED
@@ -17,7 +17,40 @@ InferenceClient(models[2]),
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  InferenceClient(models[3]),
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  ]
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  def format_prompt(message, history):
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  prompt = ""
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  if history:
@@ -42,8 +75,8 @@ def chat_inf(system_prompt,prompt,history,client_choice,seed,temp,tokens,top_p,r
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  if history:
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  hist_len=len(history)
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  print(hist_len)
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-
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- #seed = random.randint(1,1111111111111111)
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  generate_kwargs = dict(
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  temperature=temp,
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  max_new_tokens=tokens,
 
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  InferenceClient(models[3]),
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  ]
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+ ou are a Game Master for a Role Playing Game.
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+ Your duty is to narate the game and create challenges for users to overcome.
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+ Instructions
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+ - Describe the location, and scenario, in great detail
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+ - Create a situation where the user must make a choice of how to proceed
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+ - Return the scenario, and wait for the user to repond before proceeding
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+ Progress:
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+ {history}
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+ Compress the timeline of progress above into a single summary of events and progress
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+ Include all important milestones, the current challenges, and implementation details necessary to procee
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+
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+ def compress_history(history,client_choice,seed,temp,tokens,top_p,rep_p):
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+ client=clients[int(client_choice)-1]
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+ COMPRESS_HISTORY="""You are an Information Summarizer Agent. Your duty is to summarize the following information into a more concise format with far less words.
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+ Retain all the main points and provide a brief and concise summary of the conversation.
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+ Converstion:
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+ {history}"""
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+ print("COMPRESSING")
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+ formatted_prompt=f"{COMPRESS_HISTORY.format(history=history)}"
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+ generate_kwargs = dict(
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+ temperature=temp,
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+ max_new_tokens=1024,
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+ top_p=top_p,
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+ repetition_penalty=rep_p,
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+ do_sample=True,
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+ seed=seed,
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+ )
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+ stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
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+ output = ""
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+ for response in stream:
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+ output += response.token.text
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+ return output
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+
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  def format_prompt(message, history):
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  prompt = ""
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  if history:
 
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  if history:
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  hist_len=len(history)
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  print(hist_len)
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+ if hist_len>4000:
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+ history=compress_history(history,client_choice,seed,temp,tokens,top_p,rep_p)
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  generate_kwargs = dict(
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  temperature=temp,
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  max_new_tokens=tokens,