MohammedAlakhras commited on
Commit
13f7006
·
verified ·
1 Parent(s): d9c7450

Update app.py

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Files changed (1) hide show
  1. app.py +11 -21
app.py CHANGED
@@ -1,9 +1,6 @@
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  import torch
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  from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
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- import gradio as gr
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- # Define the device
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- device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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  model_id = "Narrativaai/BioGPT-Large-finetuned-chatdoctor"
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@@ -11,31 +8,21 @@ tokenizer = AutoTokenizer.from_pretrained("microsoft/BioGPT-Large")
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  model = AutoModelForCausalLM.from_pretrained(model_id)
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- # Move the model to the device
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- model = model.to(device)
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-
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  def answer_question(
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  prompt,
 
 
 
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  num_beams=2,
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  **kwargs,
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  ):
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- prompt="""
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- Below is an instruction that describes a task, paired with an input that provides further context.Write a response that appropriately completes the request.
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-
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- ### Instruction:
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- If you are a doctor, please answer the medical questions based on the patient's description.
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-
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- ### Input:
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- """+prompt+"""
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-
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- ### Response:
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- """
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  inputs = tokenizer(prompt, return_tensors="pt")
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- # Move the inputs to the device
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- inputs = {key: val.to(device) for key, val in inputs.items()}
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- input_ids = inputs["input_ids"]
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- attention_mask = inputs["attention_mask"]
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  generation_config = GenerationConfig(
 
 
 
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  num_beams=num_beams,
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  **kwargs,
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  )
@@ -66,6 +53,9 @@ Hi i have sore lumps under the skin on my legs. they started on my left ankle an
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  ### Response:
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  """
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  def gui_interface(prompt):
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  return answer_question(prompt)
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  import torch
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  from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
 
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  model_id = "Narrativaai/BioGPT-Large-finetuned-chatdoctor"
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  model = AutoModelForCausalLM.from_pretrained(model_id)
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  def answer_question(
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  prompt,
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+ temperature=0.1,
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+ top_p=0.75,
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+ top_k=40,
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  num_beams=2,
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  **kwargs,
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  ):
 
 
 
 
 
 
 
 
 
 
 
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  inputs = tokenizer(prompt, return_tensors="pt")
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+ input_ids = inputs["input_ids"].to("cuda")
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+ attention_mask = inputs["attention_mask"].to("cuda")
 
 
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  generation_config = GenerationConfig(
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+ temperature=temperature,
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+ top_p=top_p,
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+ top_k=top_k,
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  num_beams=num_beams,
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  **kwargs,
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  )
 
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  ### Response:
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  """
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+ print(answer_question(example_prompt))
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+
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+
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  def gui_interface(prompt):
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  return answer_question(prompt)
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