chat / app.py
modelflaredev's picture
Add Gradio chat app
c845c55 verified
Raw History Blame Contribute Delete
2.58 kB
import os
from openai import OpenAI
import gradio as gr
BASE_URL = "https://modelflare.dev/v1"
DEFAULT_MODELS = [
"gpt-4o-mini",
"gpt-4o",
"claude-sonnet-4-5",
"grok-4",
]
def get_client() -> OpenAI:
api_key = os.environ.get("MODELFLARE_API_KEY", "").strip()
if not api_key:
raise gr.Error(
"缺少 Space Secret:MODELFLARE_API_KEY。"
"请在 Space Settings → Variables and secrets 里添加后重启。"
)
return OpenAI(api_key=api_key, base_url=BASE_URL)
def chat(message: str, history: list, model: str, custom_model: str):
model_id = (custom_model or "").strip() or (model or "").strip() or DEFAULT_MODELS[0]
client = get_client()
messages = []
for turn in history:
if isinstance(turn, dict):
role, content = turn.get("role"), turn.get("content")
if role in ("user", "assistant") and content:
messages.append({"role": role, "content": content})
elif isinstance(turn, (list, tuple)) and len(turn) == 2:
u, a = turn
if u:
messages.append({"role": "user", "content": u})
if a:
messages.append({"role": "assistant", "content": a})
messages.append({"role": "user", "content": message})
stream = client.chat.completions.create(
model=model_id,
messages=messages,
stream=True,
)
partial = ""
for chunk in stream:
delta = chunk.choices[0].delta.content or ""
if delta:
partial += delta
yield partial
with gr.Blocks(title="Modelflare Chat Demo") as demo:
gr.Markdown(
"""
# Modelflare Chat Demo
通过 [Modelflare](https://modelflare.dev/) 统一网关试用 OpenAI 兼容对话。
文档:[docs.modelflare.dev](https://docs.modelflare.dev/) · Base URL:`https://modelflare.dev/v1`
"""
)
with gr.Row():
model = gr.Dropdown(
choices=DEFAULT_MODELS,
value=DEFAULT_MODELS[0],
label="Model",
scale=2,
)
custom_model = gr.Textbox(
label="Custom model id (可选,填写则覆盖上方)",
placeholder="例如你的分组里实际可用的模型名",
scale=3,
)
chatbot = gr.ChatInterface(
fn=chat,
additional_inputs=[model, custom_model],
examples=[
["用一句话介绍 Modelflare"],
["Write a haiku about API gateways"],
],
)
if __name__ == "__main__":
demo.queue().launch()