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import transformers
from transformers import pipeline
def generate(idea):
"""Generates code based on a given idea using the PhiCo-D-Instruk model.
Args:
idea: The idea for the code to be generated.
Returns:
The generated code as a string.
"""
# Load the code generation model
model_name = "bigscience/T0_3B" # Use a model that works for code generation
model = transformers.AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
# Generate the code
input_text = f"""
# Idea: {idea}
# Code:
"""
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output_sequences = model.generate(
input_ids=input_ids,
max_length=1024,
num_return_sequences=1,
no_repeat_ngram_size=2,
early_stopping=True,
temperature=0.7, # Adjust temperature for creativity
top_k=50, # Adjust top_k for diversity
)
generated_code = tokenizer.decode(output_sequences[0], skip_special_tokens=True)
# Remove the prompt and formatting
generated_code = generated_code.split("\n# Code:")[1].strip()
return generated_code
# Example usage
idea = "Write a Python function to calculate the factorial of a number"
code = generate(idea)
print(code) |