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import fastapi |
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import json |
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import markdown |
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import uvicorn |
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from fastapi.responses import HTMLResponse |
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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 ctransformers 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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with open("README.md", "r", encoding="utf-8") as readme_file: |
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md_template_string = readme_file.read() |
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html_content = markdown.markdown(md_template_string) |
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return HTMLResponse(content=html_content, status_code=200) |
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class ChatCompletionRequest(BaseModel): |
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prompt: str |
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@app.post("/v1/chat/completions") |
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async def chat(request: ChatCompletionRequest, response_mode=None): |
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tokens = llm.tokenize(prompt) |
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async def server_sent_events(chat_chunks, llm): |
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yield prompt |
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for chat_chunk in llm.generate(chat_chunks): |
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yield llm.detokenize(chat_chunk) |
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yield "" |
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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) |
