import fastapi
import json
import markdown
import uvicorn
from ctransformers import AutoModelForCausalLM
from fastapi.responses import HTMLResponse
from fastapi.middleware.cors import CORSMiddleware
from sse_starlette.sse import EventSourceResponse
from ctransformers.langchain import CTransformers
from pydantic import BaseModel, Field
from typing import List, Any
from typing_extensions import TypedDict, Literal
llm = AutoModelForCausalLM.from_pretrained("NeoDim/starchat-alpha-GGML",
model_file="starchat-alpha-ggml-q4_0.bin",
model_type="starcoder")
app = fastapi.FastAPI(title="Starchat Alpha")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@app.get("/")
async def index():
with open("README.md", "r", encoding="utf-8") as readme_file:
md_template_string = readme_file.read()
html_content = markdown.markdown(md_template_string)
return HTMLResponse(content=html_content, status_code=200)
class ChatCompletionRequest(BaseModel):
prompt: str
@app.get("/demo")
async def demo():
html_content = """
"""
return HTMLResponse(content=html_content, status_code=200)
@app.get("/stream")
async def chat(prompt = "Write a simple express erver"):
tokens = llm.tokenize(prompt)
async def server_sent_events(chat_chunks, llm):
yield prompt
for chat_chunk in llm.generate(chat_chunks):
yield llm.detokenize(chat_chunk)
yield ""
return EventSourceResponse(server_sent_events(tokens, llm))
@app.post("/v1/chat/completions")
async def chat(request: ChatCompletionRequest, response_mode=None):
tokens = llm.tokenize(request.prompt)
async def server_sent_events(chat_chunks, llm):
for token in llm.generate(chat_chunks):
yield llm.detokenize(token)
yield ""
return EventSourceResponse(server_sent_events(tokens, llm))
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=8000)