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acecalisto3
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caef4e5
Update code_generator.py
Browse files- code_generator.py +22 -3
code_generator.py
CHANGED
@@ -1,15 +1,24 @@
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import transformers
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def generate(idea):
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# Load the code generation model
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model_name = "
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model = transformers.AutoModelForCausalLM.from_pretrained(model_name)
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tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
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# Generate the code
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input_text = f"""
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# Idea: {idea}
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-
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# Code:
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"""
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input_ids = tokenizer.encode(input_text, return_tensors="pt")
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@@ -19,7 +28,17 @@ def generate(idea):
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num_return_sequences=1,
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no_repeat_ngram_size=2,
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early_stopping=True,
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)
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generated_code = tokenizer.decode(output_sequences[0], skip_special_tokens=True)
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-
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import transformers
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from transformers import pipeline
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def generate(idea):
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"""Generates code based on a given idea using the PhiCo-D-Instruk model.
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Args:
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idea: The idea for the code to be generated.
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Returns:
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The generated code as a string.
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"""
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# Load the code generation model
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model_name = "bigscience/T0_3B" # Use a model that works for code generation
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model = transformers.AutoModelForCausalLM.from_pretrained(model_name)
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tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
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# Generate the code
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input_text = f"""
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# Idea: {idea}
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# Code:
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"""
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input_ids = tokenizer.encode(input_text, return_tensors="pt")
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num_return_sequences=1,
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no_repeat_ngram_size=2,
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early_stopping=True,
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temperature=0.7, # Adjust temperature for creativity
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top_k=50, # Adjust top_k for diversity
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)
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generated_code = tokenizer.decode(output_sequences[0], skip_special_tokens=True)
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# Remove the prompt and formatting
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generated_code = generated_code.split("\n# Code:")[1].strip()
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return generated_code
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# Example usage
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idea = "Write a Python function to calculate the factorial of a number"
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code = generate(idea)
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print(code)
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