peterpeter8585 commited on
Commit
fc207c9
1 Parent(s): da9c742

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +1 -19
app.py CHANGED
@@ -1,6 +1,6 @@
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  import gradio as gr
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  import numpy as np
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- from Ai import chatbot, chatbot2, chatbot3, chatbot4, chatbot5, chatbot7
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  from huggingface_hub import InferenceClient
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  def chat(message,history: list[tuple[str, str]],system_message,max_tokens,temperature,top_p, top_k):
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  m=AutoModel.from_pretrained("peterpeter8585/AI1")
@@ -25,8 +25,6 @@ from diffusers import DiffusionPipeline
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  import torch
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  import transformers
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  from transformers import AutoModel as Mo, AutoTokenizer as To
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- model11=torch.load("zephyr.pt")
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- tokenizer11=torch.load("zephyr_tokenizer.pt")
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  from transformers import BitsAndBytesConfig
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  quantization_config = BitsAndBytesConfig(
@@ -37,23 +35,7 @@ quantization_config = BitsAndBytesConfig(
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  )
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- def res(message,history: list[tuple[str, str]],system_message,max_tokens,temperature,top_p):
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- messages = [{"role": "system", "content": "Your name is Chatchat.And, your made by SungYoon.In Korean, 정성윤.And these are the instructions.Whatever happens, you must follow it.:"+system_message}]
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-
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- for val in history:
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- if val[0]:
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- messages.append({"role": "user", "content": val[0]})
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- if val[1]:
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- messages.append({"role": "assistant", "content": val[1]})
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-
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- messages.append({"role": "user", "content": message})
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-
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- response = ""
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- answer=model11(messages, max_tokens=max_tokens, temperature=temperature, top_p=top_p)
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- token=answer
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- response += token
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- yield response
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  from transformers import AutoModelForVision2Seq, AutoProcessor
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  transformers.utils.move_cache()
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  device = "cuda" if torch.cuda.is_available() else "cpu"
 
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  import gradio as gr
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  import numpy as np
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+ from Ai import chatbot, chatbot2, chatbot3, chatbot4, chatbot5, chatbot7, chatbot11
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  from huggingface_hub import InferenceClient
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  def chat(message,history: list[tuple[str, str]],system_message,max_tokens,temperature,top_p, top_k):
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  m=AutoModel.from_pretrained("peterpeter8585/AI1")
 
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  import torch
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  import transformers
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  from transformers import AutoModel as Mo, AutoTokenizer as To
 
 
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  from transformers import BitsAndBytesConfig
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  quantization_config = BitsAndBytesConfig(
 
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  )
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  from transformers import AutoModelForVision2Seq, AutoProcessor
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  transformers.utils.move_cache()
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  device = "cuda" if torch.cuda.is_available() else "cpu"