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import os
import torch
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from peft import PeftModel
# 🔐 Hugging Face token (stored in repo secret as HF_TOKEN)
HF_TOKEN = os.environ.get("HF_TOKEN")
# 📌 Model sources
base_model = "mistralai/Mistral-7B-v0.1"
adapter_path = "./mistral-recovery-model" # Your uploaded LoRA adapter files
# 🚀 Load tokenizer and model in FP32 (CPU-safe)
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)
# 🧠 Pipeline
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=150, temperature=0.7)
# 🖥️ Gradio UI
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()
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