Makima57 commited on
Commit
a31692c
·
verified ·
1 Parent(s): 1503841

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +9 -14
app.py CHANGED
@@ -1,16 +1,15 @@
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-
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- import gradio as gr
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- import ctranslate2
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- from transformers import AutoTokenizer
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- from huggingface_hub import snapshot_download
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- from codeexecutor import postprocess_completion,get_majority_vote
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  # Define the model and tokenizer loading
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  model_prompt = "Solve the following mathematical problem: "
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  tokenizer = AutoTokenizer.from_pretrained("AI-MO/NuminaMath-7B-TIR")
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  model_path = snapshot_download(repo_id="Makima57/deepseek-math-Numina")
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  generator = ctranslate2.Generator(model_path, device="cpu", compute_type="int8")
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- iterations=10
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  # Function to generate predictions using the model
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  def get_prediction(question):
@@ -19,7 +18,7 @@ def get_prediction(question):
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  results = generator.generate_batch([input_tokens])
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  output_tokens = results[0].sequences[0]
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  predicted_answer = tokenizer.convert_tokens_to_string(output_tokens)
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- return predicted_answer
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  # Function to perform majority voting across multiple predictions
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  def majority_vote(question, num_iterations=10):
@@ -32,7 +31,7 @@ def majority_vote(question, num_iterations=10):
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  all_answer.append(answer)
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  majority_voted_pred = max(set(all_predictions), key=all_predictions.count)
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  majority_voted_ans=get_majority_vote(all_answer)
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- return majority_voted_pred, all_predictions,majority_voted_ans
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  # Gradio interface for user input and output
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  def gradio_interface(question, correct_answer):
@@ -57,8 +56,6 @@ demo = gr.Interface(
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  ],
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  title="Math Question Solver",
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  description="Enter a math question to get the model prediction and see all generated answers.",
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- theme="huggingface",
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- layout="vertical",
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  live=True,
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  allow_flagging="never",
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  show_input=True,
@@ -66,6 +63,4 @@ demo = gr.Interface(
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  )
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  if __name__ == "__main__":
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- interface.launch()
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-
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-
 
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+ import gradio as gr
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+ import ctranslate2
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+ from transformers import AutoTokenizer
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+ from huggingface_hub import snapshot_download
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+ from codeexecutor import postprocess_completion,get_majority_vote
 
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  # Define the model and tokenizer loading
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  model_prompt = "Solve the following mathematical problem: "
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  tokenizer = AutoTokenizer.from_pretrained("AI-MO/NuminaMath-7B-TIR")
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  model_path = snapshot_download(repo_id="Makima57/deepseek-math-Numina")
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  generator = ctranslate2.Generator(model_path, device="cpu", compute_type="int8")
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+ iterations=10
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  # Function to generate predictions using the model
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  def get_prediction(question):
 
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  results = generator.generate_batch([input_tokens])
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  output_tokens = results[0].sequences[0]
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  predicted_answer = tokenizer.convert_tokens_to_string(output_tokens)
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+ return predicted_answer
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  # Function to perform majority voting across multiple predictions
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  def majority_vote(question, num_iterations=10):
 
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  all_answer.append(answer)
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  majority_voted_pred = max(set(all_predictions), key=all_predictions.count)
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  majority_voted_ans=get_majority_vote(all_answer)
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+ return majority_voted_pred, all_predictions,majority_voted_ans
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  # Gradio interface for user input and output
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  def gradio_interface(question, correct_answer):
 
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  ],
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  title="Math Question Solver",
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  description="Enter a math question to get the model prediction and see all generated answers.",
 
 
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  live=True,
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  allow_flagging="never",
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  show_input=True,
 
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  )
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  if __name__ == "__main__":
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+ demo.launch()