dnnsdunca/droidlabs-Agentic-bert

1import os2from transformers import AutoModelForSeq2SeqLM, AutoTokenizer3from transformers import pipeline4model_name = "dbernsohn/roberta-java"5model = AutoModelForSeq2SeqLM.from_pretrained(model_name)6tokenizer = AutoTokenizer.from_pretrained(model_name)7def preprocess_input(description):8    input_text = "Generate an agent that " + description9    inputs = tokenizer.encode(input_text, return_tensors='pt')10    return inputs11    def generate_agent_code(inputs):12    generated_ids = model.generate(inputs)13    agent_code = tokenizer.decode(generated_ids[0], skip_special_tokens=True)14    return agent_code15    import os16from transformers import AutoModelForSeq2SeqLM, AutoTokenizer17from transformers import pipeline18 19# Load the pre-trained CodeBERTa model and tokenizer20model_name = "dbernsohn/roberta-java"21model = AutoModelForSeq2SeqLM.from_pretrained(model_name)22tokenizer = AutoTokenizer.from_pretrained(model_name)23 24# Function to pre-process user input description25def preprocess_input(description):26    input_text = "Generate an agent that " + description27    inputs = tokenizer.encode(input_text, return_tensors='pt')28    return inputs29 30# Function to generate agent code using the fine-tuned model31def generate_agent_code(inputs):32    generated_ids = model.generate(inputs)33    agent_code = tokenizer.decode(generated_ids[0], skip_special_tokens=True)34    return agent_code35 36# Example usage37user_description = "can perform sentiment analysis on text data."38inputs = preprocess_input(user_description)39generated_code = generate_agent_code(inputs)40print(generated_code)41