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import gradio as gr |
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from datasets import load_dataset |
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from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments |
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dataset = load_dataset("json", data_files="dataset.jsonl") |
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model_name = "Salesforce/codegen-2B-multi" |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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def tokenize_function(examples): |
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return tokenizer(examples["input"], text_target=examples["output"], truncation=True) |
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tokenized_dataset = dataset.map(tokenize_function, batched=True) |
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training_args = TrainingArguments( |
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output_dir="./results", |
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overwrite_output_dir=True, |
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eval_strategy="epoch", |
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learning_rate=5e-5, |
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per_device_train_batch_size=2, |
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num_train_epochs=3, |
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save_strategy="epoch", |
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logging_dir="./logs", |
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logging_strategy="epoch", |
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) |
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trainer = Trainer( |
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model=model, |
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args=training_args, |
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train_dataset=tokenized_dataset["train"], |
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eval_dataset=tokenized_dataset["train"], |
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) |
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trainer.train() |
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trainer.save_model("./fine_tuned_model") |
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tokenizer.save_pretrained("./fine_tuned_model") |
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fine_tuned_model = AutoModelForCausalLM.from_pretrained("./fine_tuned_model") |
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fine_tuned_tokenizer = AutoTokenizer.from_pretrained("./fine_tuned_model") |
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def generate_cypress_code(prompt): |
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inputs = fine_tuned_tokenizer(prompt, return_tensors="pt") |
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outputs = fine_tuned_model.generate(inputs["input_ids"], max_length=150, num_return_sequences=1) |
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return fine_tuned_tokenizer.decode(outputs[0], skip_special_tokens=True) |
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interface = gr.Interface( |
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fn=generate_cypress_code, |
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inputs="text", |
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outputs="text", |
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title="Cypress Test Generator", |
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description="Enter a description of the test you want to generate Cypress code for.", |
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) |
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interface.launch() |