import fastapi
import json
import markdown
import uvicorn
from fastapi.responses import HTMLResponse
from fastapi.middleware.cors import CORSMiddleware
from sse_starlette.sse import EventSourceResponse
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from ctransformers import AutoModelForCausalLM
from pydantic import BaseModel

config = {"max_seq_len": 4096}
llm = AutoModelForCausalLM.from_pretrained('TheBloke/MPT-7B-Storywriter-GGML',
                                           model_file='mpt-7b-storywriter.ggmlv3.q4_0.bin',
                                           model_type='mpt')
app = fastapi.FastAPI()
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.post("/v1/chat/completions")
async def chat(request: ChatCompletionRequest, response_mode=None):
    completion = llm(request.prompt)
    async def server_sent_events(chat_chunks):
        for chat_chunk in chat_chunks:
            yield dict(data=json.dumps(chat_chunk))
        yield dict(data="[DONE]")

    return EventSourceResponse(server_sent_events(completion))

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