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import json |
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import markdown |
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from typing import List, Dict, Any, Generator |
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from functools import partial |
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import fastapi |
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import uvicorn |
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from fastapi import HTTPException, Depends, Request |
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from fastapi.responses import HTMLResponse, StreamingResponse |
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from fastapi.middleware.cors import CORSMiddleware |
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from sse_starlette.sse import EventSourceResponse |
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from anyio import create_memory_object_stream, run_in_threadpool |
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from transformers import AutoModelForCausalLM |
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from pydantic import BaseModel |
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llm = AutoModelForCausalLM.from_pretrained("TheBloke/WizardCoder-15B-1.0-GGML", |
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model_file="WizardCoder-15B-1.0.ggmlv3.q4_0.bin", |
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model_type="starcoder") |
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app = fastapi.FastAPI(title="🪄WizardCoder💫") |
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app.add_middleware( |
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CORSMiddleware, |
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allow_origins=["*"], |
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allow_credentials=True, |
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allow_methods=["*"], |
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allow_headers=["*"], |
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) |
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@app.get("/") |
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async def index(): |
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html_content = """ |
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<html> |
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<head> |
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</head> |
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<body style="background-color:black"> |
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<h2 style="font-family:system-ui"><a href="https://huggingface.co/TheBloke/WizardCoder-15B-1.0-GGML">wizardcoder-ggml</a></h2> |
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<iframe |
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src="https://matthoffner-monacopilot.hf.space" |
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frameborder="0" |
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width="95%" |
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height="90%" |
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></iframe> |
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<h2 style="font-family:system-ui"><a href="https://matthoffner-wizardcoder-ggml.hf.space/docs">FastAPI Docs</a></h2> |
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</body> |
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</html> |
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""" |
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return HTMLResponse(content=html_content, status_code=200) |
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class ChatCompletionRequestV0(BaseModel): |
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prompt: str |
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class Message(BaseModel): |
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role: str |
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content: str |
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class ChatCompletionRequest(BaseModel): |
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messages: List[Message] |
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max_tokens: int = 250 |
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@app.post("/v1/completions") |
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async def completion(request: ChatCompletionRequestV0, response_mode=None): |
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response = llm(request.prompt) |
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return response |
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@app.post("/v1/chat/completions") |
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async def chat(request: ChatCompletionRequest): |
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combined_messages = ' '.join([message.content for message in request.messages]) |
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tokens = llm.tokenize(combined_messages) |
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try: |
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chat_chunks = llm.generate(tokens) |
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except Exception as e: |
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raise HTTPException(status_code=500, detail=str(e)) |
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async def format_response(chat_chunks: Generator) -> Any: |
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for chat_chunk in chat_chunks: |
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response = { |
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'choices': [ |
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{ |
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'message': { |
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'role': 'system', |
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'content': llm.detokenize(chat_chunk) |
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}, |
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'finish_reason': 'stop' if llm.detokenize(chat_chunk) == "[DONE]" else 'unknown' |
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} |
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] |
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} |
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yield f"data: {json.dumps(response)}\n\n" |
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yield "event: done\ndata: {}\n\n" |
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return StreamingResponse(format_response(chat_chunks), media_type="text/event-stream") |
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@app.post("/v2/chat/completions") |
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async def chatV2(request: Request, body: ChatCompletionRequest): |
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combined_messages = ' '.join([message.content for message in body.messages]) |
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tokens = llm.tokenize(combined_messages) |
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send_chan, recv_chan = create_memory_object_stream(10) |
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async def event_publisher(inner_send_chan): |
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async with inner_send_chan: |
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try: |
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iterator: Generator = await run_in_threadpool(llm.generate, tokens) |
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for chat_chunk in iterator: |
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response = { |
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'choices': [ |
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{ |
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'message': { |
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'role': 'system', |
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'content': llm.detokenize(chat_chunk) |
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}, |
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'finish_reason': 'stop' if llm.detokenize(chat_chunk) == "[DONE]" else 'unknown' |
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} |
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] |
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} |
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await inner_send_chan.send(f"data: {json.dumps(response)}\n\n") |
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await inner_send_chan.send("event: done\ndata: {}\n\n") |
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except Exception as e: |
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print(f"Exception in event publisher: {str(e)}") |
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return StreamingResponse(recv_chan, media_type="text/event-stream", data_sender_callable=partial(event_publisher, send_chan)) |
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@app.post("/v0/chat/completions") |
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async def chat(request: ChatCompletionRequestV0, response_mode=None): |
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tokens = llm.tokenize(request.prompt) |
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async def server_sent_events(chat_chunks, llm): |
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for chat_chunk in llm.generate(chat_chunks): |
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yield dict(data=json.dumps(llm.detokenize(chat_chunk))) |
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yield dict(data="[DONE]") |
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return EventSourceResponse(server_sent_events(tokens, llm)) |
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if __name__ == "__main__": |
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uvicorn.run(app, host="0.0.0.0", port=8000) |
