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import gradio as gr
from huggingface_hub import InferenceClient
from fastapi import FastAPI
from pydantic import BaseModel
import uvicorn
# Hugging Face model
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# FastAPI app
app = FastAPI()
# Request format
class Request(BaseModel):
message: str
history: list[tuple[str, str]] = []
system_message: str = "You are a friendly chatbot."
max_tokens: int = 512
temperature: float = 0.7
top_p: float = 0.95
@app.post("/chat") # ✅ This makes the API work with Roblox!
def chat(req: Request):
messages = [{"role": "system", "content": req.system_message}]
for user_msg, bot_reply in req.history:
if user_msg:
messages.append({"role": "user", "content": user_msg})
if bot_reply:
messages.append({"role": "assistant", "content": bot_reply})
messages.append({"role": "user", "content": req.message})
response_text = ""
for message in client.chat_completion(
messages,
max_tokens=req.max_tokens,
stream=True,
temperature=req.temperature,
top_p=req.top_p
):
token = message.choices[0].delta.content
response_text += token
return {"response": response_text} # ✅ Returns plain text response
# ✅ Gradio Interface (optional, can be removed if using FastAPI only)
def respond(message, history, system_message, max_tokens, temperature, top_p):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
],
)
# Run both Gradio and FastAPI
if __name__ == "__main__":
import threading
def run_gradio():
demo.launch(share=True) # ✅ Keep Gradio running
def run_fastapi():
uvicorn.run(app, host="0.0.0.0", port=7860)
threading.Thread(target=run_gradio).start()
run_fastapi()
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