thepolymerguy commited on
Commit
94f6c5d
1 Parent(s): 4649b38

Upload app.py

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Files changed (1) hide show
  1. app.py +23 -20
app.py CHANGED
@@ -37,6 +37,21 @@ model = LlamaForCausalLM.from_pretrained(
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  device_map='auto',
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  )
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  ########## DEFINING FUNCTIONS ###################
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  def mean_pooling(model_output, attention_mask):
@@ -189,6 +204,8 @@ def classifier(userin, SearchType):
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  md_class = '\n'.join(class_links)
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  return md_class
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  def generateresponse(history, temp, top_p, tokens):
@@ -219,10 +236,7 @@ def generateresponse(history, temp, top_p, tokens):
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  response = f"Response{outputs}"
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- f = open(os.path.join(os.path.dirname(__file__), "Model_bert/log.log"), "a")
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- count = 1
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- f.write(f'{count}\n')
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- f.close()
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  return response
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@@ -254,10 +268,7 @@ def run_model(userin, dropd, temp, top_p, tokens):
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  response = f"Response{outputs}"
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- f = open(os.path.join(os.path.dirname(__file__), "Model_bert/log.log"), "a")
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- count = 1
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- f.write(f'{count}\n')
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- f.close()
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  return response
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@@ -293,10 +304,7 @@ def prosecute(application, priorart, temp, top_p, tokens):
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  response = f"Response{outputs}"
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- f = open(os.path.join(os.path.dirname(__file__), "Model_bert/log.log"), "a")
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- count = 1
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- f.write(f'{count}\n')
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- f.close()
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  return response
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@@ -329,10 +337,7 @@ def ideator(userin, temp, top_p, tokens):
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  response = f"Response{outputs}"
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- f = open(os.path.join(os.path.dirname(__file__), "Model_bert/log.log"), "a")
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- count = 1
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- f.write(f'{count}\n')
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- f.close()
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  return response
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@@ -364,10 +369,7 @@ def Chat(userin, temp, top_p, tokens):
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  response = f"Response{outputs}"
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- f = open(os.path.join(os.path.dirname(__file__), "Model_bert/log.log"), "a")
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- count = 1
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- f.write(f'{count}\n')
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- f.close()
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  return response
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@@ -563,3 +565,4 @@ with gr.Blocks(title='Claimed', theme=theme) as demo:
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  """)
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  demo.queue(max_size=15)
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  demo.launch()
 
 
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  device_map='auto',
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  )
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+
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+ DATASET_REPO_URL = "https://huggingface.co/datasets/Claimed/logger"
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+ DATA_FILENAME = "data.csv"
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+ DATA_FILE = os.path.join("data", DATA_FILENAME)
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+
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+ def store_log():
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+ with open(DATA_FILE, "a") as csvfile:
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+ writer = csv.DictWriter(csvfile, fieldnames=["count", "time"])
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+ writer.writerow(
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+ {"count": 1, "time": str(datetime.now())}
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+ )
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+ commit_url = repo.push_to_hub()
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+ print(commit_url)
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+ return
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+
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  ########## DEFINING FUNCTIONS ###################
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  def mean_pooling(model_output, attention_mask):
 
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  md_class = '\n'.join(class_links)
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+ store_log()
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+
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  return md_class
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  def generateresponse(history, temp, top_p, tokens):
 
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  response = f"Response{outputs}"
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+ store_log()
 
 
 
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  return response
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  response = f"Response{outputs}"
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+ store_log()
 
 
 
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  return response
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  response = f"Response{outputs}"
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+ store_log()
 
 
 
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  return response
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  response = f"Response{outputs}"
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+ store_log()
 
 
 
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  return response
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  response = f"Response{outputs}"
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+ store_log()
 
 
 
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  return response
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  """)
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  demo.queue(max_size=15)
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  demo.launch()
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+