app.py

import os, gradio as gr
from openai import OpenAI

# 初始化 DeepSeek 客户端
client = OpenAI(
    api_key=os.getenv("DEEPSEEK_API_KEY"),
    base_url="https://api.deepseek.com/v1"
)

def analyze_stream(temperature, light, soil_humidity):
    """
    接收三个环境数据指标，调用 DeepSeek chat 模型进行流式分析。
    将生成的 token 实时 yield 回前端。
    """
    messages = [
        {"role": "system", "content": "You are a plant care assistant."},
        {"role": "user", "content": 
            f"Temperature: {temperature}°C, Light: {light} lux, Soil Humidity: {soil_humidity}%."}
    ]
    stream = client.chat.completions.create(
        model="deepseek-chat",
        messages=messages,
        temperature=0.7,
        stream=True
    )
    partial = ""
    for chunk in stream:
        delta = chunk.choices[0].delta.content or ""
        partial += delta
        yield partial  # 持续输出拼接中的内容

# 定义 Gradio 界面
demo = gr.Interface(
    fn=analyze_stream,
    inputs=[
        gr.Number(label="Temperature (°C)"),
        gr.Number(label="Light (lux)"),
        gr.Number(label="Soil Humidity (%)")
    ],
    outputs=gr.Textbox(label="DeepSeek 分析结果"),
    title="🌱 实时植物环境分析",
    description="输入当前环境参数后，系统将流式返回 DeepSeek 的分析建议。",
)

# 启用 Gradio 的队列和流式输出
demo.queue(concurrency_count=2).launch(server_name="0.0.0.0")