Rajut commited on
Commit
747f39c
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1 Parent(s): a5dd405

Update app.py

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Files changed (1) hide show
  1. app.py +16 -18
app.py CHANGED
@@ -1,9 +1,9 @@
1
  import streamlit as st
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  import torch
 
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  import csv
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  import re
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  import warnings
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- from transformers import pipeline
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  warnings.filterwarnings("ignore")
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@@ -14,15 +14,17 @@ MAGICODER_PROMPT = """You are an exceptionally intelligent coding assistant that
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  @@ Response
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  """
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- # Create a text generation pipeline using the Magicoder model, text-generation task, bfloat16 torch data type and auto device mapping.
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- generator = torch.quantization.quantize_dynamic(
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- pipeline(
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- model="ise-uiuc/Magicoder-S-DS-6.7B",
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- task="text-generation",
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- ),
 
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  {torch.nn.Linear},
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  dtype=torch.qint8
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  )
 
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  # Function to generate response
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  def generate_response(instruction):
@@ -39,16 +41,6 @@ def save_to_csv(data, filename):
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  writer = csv.writer(csvfile)
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  writer.writerow(data)
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- # Function to process user feedback
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- def process_output(correct_output):
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- if correct_output.lower() == 'yes':
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- feedback = st.text_input("Do you want to provide any feedback?")
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- save_to_csv(["Correct", feedback], 'output_ratings.csv')
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- else:
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- correct_code = st.text_area("Please enter the correct code:")
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- feedback = st.text_input("Any other feedback you want to provide:")
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- save_to_csv(["Incorrect", feedback, correct_code], 'output_ratings.csv')
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-
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  # Streamlit app
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  def main():
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  st.title("Magicoder Assistant")
@@ -60,7 +52,13 @@ def main():
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  st.text(generated_response)
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  correct_output = st.radio("Is the generated output correct?", ("Yes", "No"))
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- process_output(correct_output)
 
 
 
 
 
 
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  if __name__ == "__main__":
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  main()
 
1
  import streamlit as st
2
  import torch
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+ from transformers import pipeline
4
  import csv
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  import re
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  import warnings
 
7
 
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  warnings.filterwarnings("ignore")
9
 
 
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  @@ Response
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  """
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+ # Load Magicoder model and apply dynamic quantization
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+ generator = pipeline(
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+ model="ise-uiuc/Magicoder-S-DS-6.7B",
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+ task="text-generation",
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+ )
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+ quantized_generator = torch.quantization.quantize_dynamic(
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+ generator.model,
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  {torch.nn.Linear},
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  dtype=torch.qint8
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  )
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+ generator.model = quantized_generator
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  # Function to generate response
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  def generate_response(instruction):
 
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  writer = csv.writer(csvfile)
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  writer.writerow(data)
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  # Streamlit app
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  def main():
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  st.title("Magicoder Assistant")
 
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  st.text(generated_response)
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  correct_output = st.radio("Is the generated output correct?", ("Yes", "No"))
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+ if correct_output.lower() == 'yes':
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+ feedback = st.text_input("Do you want to provide any feedback?")
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+ save_to_csv(["Correct", feedback], 'output_ratings.csv')
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+ else:
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+ correct_code = st.text_area("Please enter the correct code:")
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+ feedback = st.text_input("Any other feedback you want to provide:")
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+ save_to_csv(["Incorrect", feedback, correct_code], 'output_ratings.csv')
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  if __name__ == "__main__":
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  main()