app.py

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import gradio as gr

torch.random.manual_seed(0)

model = AutoModelForCausalLM.from_pretrained(
    "savage1221/lora-fine", 
    # device_map="cuda", 
    # torch_dtype="auto", 
    trust_remote_code=True, 
)
tokenizer = AutoTokenizer.from_pretrained("savage1221/lora-fine",trust_remote_code=True)

instruction = "Generate quotes for AWS RDS services"

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
)

generation_args = {
    "max_new_tokens": 500,
    "return_full_text": False,
    "temperature": 0.9,
    "do_sample": True,
    "top_k": 50,
    "top_p": 0.95,
    "num_return_sequences": 1,
}

def predict_price(input_data):
    prompt = f"{instruction}\nInput: {input_data}\nOutput:"
    output = pipe(prompt, **generation_args)
    return output[0]['generated_text']

interface = gr.Interface(
    fn=predict_price,
    inputs=gr.inputs.Textbox(lines=7, label="输入商品信息"),
    outputs=gr.outputs.Textbox(label="预测价格"),
    title="商品价格预测",
    description="输入商品信息,预测商品价格",
)

interface.launch()