| import gradio as gr | |
| from transformers import pipeline | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| def generateYodaLanguage(prompt): | |
| repo_id="dvgodoy/phi3-mini-yoda-adapter" | |
| model = AutoModelForCausalLM.from_pretrained (repo_id) | |
| tokenizer = AutoTokenizer.from_pretrained(repo_id) | |
| generator = pipeline("text-generation", model=repo_id) | |
| output = generator([{"role": "user", "content": prompt}], max_new_tokens=128, return_full_text=False)[0] | |
| return output["generated_text"] | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| text=gr.Textbox(label="prompt") | |
| translate_btn=gr.Button("Say it in Yoda Language!") | |
| output=gr.Textbox(label="Yoda language") | |
| translate_btn.click(fn=generateYodaLanguage, inputs=text, outputs=output) | |
| demo.launch(debug=True,share=True) |