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import gradio as gr |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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model_id = "your-username/lora-plant-deepseek" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto") |
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def plant_ask(user_input): |
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prompt = f"用户提问：{user_input}\n请用人性化语言回答，并建议一些可查阅的植物文献资料。\n回答：" |
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
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outputs = model.generate(**inputs, max_new_tokens=300) |
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return tokenizer.decode(outputs[0], skip_special_tokens=True) |
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iface = gr.Interface( |
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fn=plant_ask, |
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inputs="text", |
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outputs="text", |
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title="🌱 植物助手问答系统", |
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description="欢迎提问关于植物养护、生长环境、病虫害防治等问题，我会尽力给出人性化建议和文献推荐。", |
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) |
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iface.launch() |
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