import gradio as gr
import os
from openai import OpenAI

# 从环境变量读取两个 API Key
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY")

# 初始化两个客户端
openai_client = OpenAI(api_key=OPENAI_API_KEY)
deepseek_client = OpenAI(
    api_key=DEEPSEEK_API_KEY,
    # 如果 DeepSeek 的 API 路径需要带 /v1，可以根据实际文档调整
    base_url="https://api.deepseek.com/v1"
)

def generate_response(model_provider, prompt, temperature, top_p, max_tokens, repetition_penalty):
    # 根据 model_provider 分发到对应 client 和 model 名称
    clients = {
        "DeepSeek": (deepseek_client, "deepseek-chat"),
        "OpenAI":   (openai_client,   "gpt-3.5-turbo")
    }
    client, model = clients[model_provider]
    
    try:
        resp = client.chat.completions.create(
            model=model,
            messages=[{"role": "user", "content": prompt}],
            temperature=temperature,
            top_p=top_p,
            max_tokens=max_tokens,
            # repetition_penalty 建议映射到 frequency_penalty 或 presence_penalty，根据需求选一个
            frequency_penalty=repetition_penalty,
            presence_penalty=0.0
        )
        return resp.choices[0].message.content.strip()
    except Exception as e:
        return f"{model_provider} API Error: {e}"

with gr.Blocks(theme=gr.themes.Soft()) as iface:
    gr.Markdown("## 🧠 DeepSeek / OpenAI 聊天演示（可调参）")
    with gr.Row():
        provider = gr.Dropdown(
            choices=["DeepSeek", "OpenAI"],
            value="DeepSeek",
            label="模型供应商"
        )
        temperature = gr.Slider(
            minimum=0.1, maximum=1.5, step=0.1, value=0.7,
            label="Temperature"
        )
        top_p = gr.Slider(
            minimum=0.1, maximum=1.0, step=0.05, value=0.9,
            label="Top-p"
        )
    prompt = gr.Textbox(
        label="Prompt",
        lines=6,
        placeholder="在这里输入你的问题……"
    )
    with gr.Row():
        max_tokens = gr.Slider(
            minimum=32, maximum=2048, step=32, value=512,
            label="Max Tokens"
        )
        rep_penalty = gr.Slider(
            minimum=0.0, maximum=2.0, step=0.1, value=1.1,
            label="Frequency Penalty"
        )
    output = gr.Textbox(label="Response")
    btn = gr.Button("生成回答")
    btn.click(
        fn=generate_response,
        inputs=[provider, prompt, temperature, top_p, max_tokens, rep_penalty],
        outputs=output
    )

iface.launch()