main.py

import json
from typing import List
import fastapi
import markdown
import uvicorn
from ctransformers import AutoModelForCausalLM
from fastapi import HTTPException
from fastapi.responses import HTMLResponse
from fastapi.middleware.cors import CORSMiddleware
from sse_starlette.sse import EventSourceResponse
from pydantic import BaseModel, Field
from typing_extensions import Literal
from dialogue import DialogueTemplate

llm = AutoModelForCausalLM.from_pretrained("TheBloke/starchat-beta-GGML",
                                           model_file="starchat-beta.ggmlv3.q4_0.bin",
                                           model_type="starcoder")

app = fastapi.FastAPI(title="Starchat Beta")
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)


@app.get("/stream")
async def chat(prompt = "<|user|> Write an express server with server sent events. <|assistant|>"):
    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))


class ChatCompletionRequestMessage(BaseModel):
    role: Literal["system", "user", "assistant"] = Field(
        default="user", description="The role of the message."
    )
    content: str = Field(default="", description="The content of the message.")

class ChatCompletionRequest(BaseModel):
    messages: List[ChatCompletionRequestMessage] = Field(
        default=[], description="A list of messages to generate completions for."
    )

system_message = "Below is a conversation between a human user and a helpful AI coding assistant."

@app.post("/v1/chat/completions")
async def chat(request: ChatCompletionRequest):
    kwargs = request.dict()
    dialogue_template = DialogueTemplate(
        system=system_message, messages=kwargs['messages']
    )
    prompt = dialogue_template.get_inference_prompt()
    tokens = llm.tokenize(combined_messages)
    
    try:
        chat_chunks = llm.generate(tokens)
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

    async def format_response(chat_chunks: Generator) -> Any:
        for chat_chunk in chat_chunks:
            response = {
                'choices': [
                    {
                        'message': {
                            'role': 'system',
                            'content': llm.detokenize(chat_chunk)
                        },
                        'finish_reason': 'stop' if llm.detokenize(chat_chunk) == "[DONE]" else 'unknown'
                    }
                ]
            }
            yield f"data: {json.dumps(response)}\n\n"
        yield "event: done\ndata: {}\n\n"

    return EventSourceResponse(format_response(chat_chunks), media_type="text/event-stream")

@app.post("/v0/chat/completions")
async def chatV0(request: ChatCompletionRequest, response_mode=None):
    kwargs = request.dict()
    dialogue_template = DialogueTemplate(
        system=system_message, messages=kwargs['messages']
    )
    prompt = dialogue_template.get_inference_prompt()
    tokens = llm.tokenize(prompt)
    async def server_sent_events(chat_chunks, llm):
        for token in llm.generate(chat_chunks):
            yield dict(data=llm.detokenize(token))
        yield dict(data="[DONE]")

    return EventSourceResponse(server_sent_events(tokens, llm))

if __name__ == "__main__":
  uvicorn.run(app, host="0.0.0.0", port=8000)