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import json
import gradio as gr
import requests
import uvicorn
from fastapi import FastAPI
from huggingface_hub import InferenceClient
from starlette.responses import StreamingResponse, JSONResponse
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("microsoft/Phi-3-mini-4k-instruct")
def respond(
message,
history: list[tuple[str, str]],
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
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
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 (nucleus sampling)",
),
],
)
app = FastAPI()
@app.head("/ask")
def ask_head():
return StreamingResponse("", media_type="application/json")
@app.get("/ask")
def ask_get(message: str = "", system_message: str = "You are a friendly Chatbot.", max_tokens: int = 512, temperature: float = 0.7, top_p: float = 0.95):
predict_response = requests.post('http://localhost:7860/call/chat', json={'data': [message, [], system_message, max_tokens, temperature, top_p]}).json()
if "event_id" not in predict_response:
return predict_response
out = requests.get(f'http://localhost:7860/call/chat/{predict_response["event_id"]}').text
return JSONResponse([json.loads(out.rsplit("event: complete\ndata: ", 1)[-1])[0].strip()])
if __name__ == "__main__":
app = gr.mount_gradio_app(app, demo, path="/")
uvicorn.run(app, host="0.0.0.0", port=7860) |