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| 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=["*"], | |
| ) | |
| 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) | |
| 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." | |
| 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") | |
| 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) | |