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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 = """
    <!DOCTYPE html>
    <html>
        <body>
            <div id="content"></div>
            
            <script>
              var source = new EventSource("https://matthoffner-starchat-alpha.hf.space/stream");
              source.onmessage = function(event) {
                document.getElementById("content").innerHTML += event.data
              };
            </script>
        
        </body>
    </html>
    """
    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)