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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 | |
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=["*"], | |
) | |
async def index(): | |
html_content = """ | |
<html> | |
<head> | |
</head> | |
<body style="font-family:system-ui"> | |
<h2><a href="https://huggingface.co/TheBloke/WizardCoder-15B-1.0-GGML">wizardcoder-ggml</a></h2> | |
<h2><a href="https://matthoffner-wizardcoder-ggml.hf.space/docs">FastAPI Docs</a></h2> | |
<h2><a href="https://wizardcoder-sandbox.netlify.app">Wizardcoder Sandbox</a></h2> | |
<h2><a href="https://matthoffner-monacopilot.hf.space">monacopilot</a></h2> | |
</body> | |
</html> | |
""" | |
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 | |
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]") | |
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, llm): | |
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 dict(data=json.dumps(response)) | |
yield dict(data="[DONE]") | |
except Exception as e: | |
print(f"Exception in event publisher: {str(e)}") | |
async def chatV2_endpoint(request: Request, body: ChatCompletionRequest): | |
combined_messages = ' '.join([message.content for message in body.messages]) | |
tokens = llm.tokenize(combined_messages) | |
return EventSourceResponse(stream_response(tokens, llm)) | |
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) | |