| import os |
| import torch |
| import gradio as gr |
| from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline |
| from peft import PeftModel |
|
|
| |
| HF_TOKEN = os.environ.get("HF_TOKEN") |
|
|
| |
| base_model = "mistralai/Mistral-7B-v0.1" |
| adapter_path = "./mistral-recovery-model" |
|
|
| |
| tokenizer = AutoTokenizer.from_pretrained(base_model, use_auth_token=HF_TOKEN) |
| tokenizer.pad_token = tokenizer.eos_token |
|
|
| model = AutoModelForCausalLM.from_pretrained( |
| base_model, |
| torch_dtype=torch.float32, |
| use_auth_token=HF_TOKEN |
| ) |
|
|
| model = PeftModel.from_pretrained(model, adapter_path, use_auth_token=HF_TOKEN) |
|
|
| |
| pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=150, temperature=0.7) |
|
|
| |
| def generate(prompt): |
| return pipe(prompt)[0]['generated_text'] |
|
|
| gr.Interface( |
| fn=generate, |
| inputs="text", |
| outputs="text", |
| title="🏋️ Mistral Recovery Coach (CPU Mode)", |
| description="Enter your workout summary and get personalized recovery advice." |
| ).launch() |
|
|
