Agent-bert.py

import os
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from transformers import pipeline
model_name = "dbernsohn/roberta-java"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
def preprocess_input(description):
    input_text = "Generate an agent that " + description
    inputs = tokenizer.encode(input_text, return_tensors='pt')
    return inputs
    def generate_agent_code(inputs):
    generated_ids = model.generate(inputs)
    agent_code = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
    return agent_code
    import os
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from transformers import pipeline

# Load the pre-trained CodeBERTa model and tokenizer
model_name = "dbernsohn/roberta-java"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Function to pre-process user input description
def preprocess_input(description):
    input_text = "Generate an agent that " + description
    inputs = tokenizer.encode(input_text, return_tensors='pt')
    return inputs

# Function to generate agent code using the fine-tuned model
def generate_agent_code(inputs):
    generated_ids = model.generate(inputs)
    agent_code = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
    return agent_code

# Example usage
user_description = "can perform sentiment analysis on text data."
inputs = preprocess_input(user_description)
generated_code = generate_agent_code(inputs)
print(generated_code)