import json import markdown from typing import Callable, List, Dict, Any, Generator from functools import partial import fastapi import uvicorn from fastapi import HTTPException, Depends, Request from fastapi.responses import HTMLResponse, StreamingResponse from fastapi.middleware.cors import CORSMiddleware from sse_starlette.sse import EventSourceResponse from anyio import create_memory_object_stream from anyio.to_thread import run_sync from ctransformers import AutoModelForCausalLM from pydantic import BaseModel llm = AutoModelForCausalLM.from_pretrained("TheBloke/WizardCoder-15B-1.0-GGML", model_file="WizardCoder-15B-1.0.ggmlv3.q5_0.bin", model_type="starcoder", threads=8) app = fastapi.FastAPI(title="🪄WizardCoder💫") app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) @app.get("/") async def index(): html_content = """

wizardcoder-ggml

FastAPI Docs

Wizardcoder Sandbox

monacopilot

""" return HTMLResponse(content=html_content, status_code=200) class ChatCompletionRequestV0(BaseModel): prompt: str class Message(BaseModel): role: str content: str class ChatCompletionRequest(BaseModel): messages: List[Message] max_tokens: int = 250 @app.post("/v1/completions") async def completion(request: ChatCompletionRequestV0, response_mode=None): response = llm(request.prompt) return response async def generate_response(chat_chunks, llm): for chat_chunk in chat_chunks: response = { 'choices': [ { 'message': { 'role': 'system', 'content': llm.detokenize(chat_chunk) }, 'finish_reason': 'stop' if llm.is_eos_token(chat_chunk) else 'unknown' } ] } yield dict(data=json.dumps(response)) yield dict(data="[DONE]") @app.post("/v1/chat/completions") async def chat(request: ChatCompletionRequest): combined_messages = ' '.join([message.content for message in request.messages]) tokens = llm.tokenize(combined_messages) try: chat_chunks = llm.generate(tokens) except Exception as e: raise HTTPException(status_code=500, detail=str(e)) return EventSourceResponse(generate_response(chat_chunks, llm)) async def stream_response(tokens: Any) -> None: try: iterator: Generator = llm.generate(tokens) for chat_chunk in iterator: response = { 'choices': [ { 'message': { 'role': 'system', 'content': llm.detokenize(chat_chunk) }, 'finish_reason': 'stop' if llm.is_eos_token(chat_chunk) else 'unknown' } ] } yield f"data: {json.dumps(response)}\n\n" yield b"event: done\ndata: {}\n\n" except Exception as e: print(f"Exception in event publisher: {str(e)}") async def chatV2(request: Request, body: ChatCompletionRequest): combined_messages = ' '.join([message.content for message in body.messages]) tokens = llm.tokenize(combined_messages) return StreamingResponse(stream_response(tokens)) @app.post("/v2/chat/completions") async def chatV2_endpoint(request: Request, body: ChatCompletionRequest): return await chatV2(request, body) @app.post("/v0/chat/completions") async def chat(request: ChatCompletionRequestV0, response_mode=None): tokens = llm.tokenize(request.prompt) async def server_sent_events(chat_chunks, llm): for chat_chunk in llm.generate(chat_chunks): yield dict(data=json.dumps(llm.detokenize(chat_chunk))) yield dict(data="[DONE]") return EventSourceResponse(server_sent_events(tokens, llm)) if __name__ == "__main__": uvicorn.run(app, host="0.0.0.0", port=8000)