| import gradio as gr |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| from peft import PeftModel |
|
|
| |
| tokenizer = AutoTokenizer.from_pretrained("hariom329/Fashionista") |
| model = AutoModelForCausalLM.from_pretrained("hariom329/Fashionista") |
| model = PeftModel.from_pretrained(model, "hariom329/Fashionista") |
|
|
| def answer(query): |
| inputs = tokenizer(query, return_tensors="pt") |
| outputs = model.generate(**inputs, max_new_tokens=200) |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
| return response |
|
|
| |
| iface = gr.Interface(fn=answer, inputs="text", outputs="text", title="Fashion Assistant") |
| iface.launch() |
|
|
