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| import fastapi | |
| import json | |
| import markdown | |
| import uvicorn | |
| from ctransformers import AutoModelForCausalLM | |
| from fastapi.responses import HTMLResponse | |
| from fastapi.middleware.cors import CORSMiddleware | |
| from sse_starlette.sse import EventSourceResponse | |
| from ctransformers.langchain import CTransformers | |
| from pydantic import BaseModel, Field | |
| from typing import List, Any | |
| from typing_extensions import TypedDict, Literal | |
| llm = AutoModelForCausalLM.from_pretrained("NeoDim/starchat-alpha-GGML", | |
| model_file="starchat-alpha-ggml-q4_0.bin", | |
| model_type="starcoder") | |
| app = fastapi.FastAPI(title="Starchat Alpha") | |
| 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) | |
| class ChatCompletionRequest(BaseModel): | |
| prompt: str | |
| async def demo(): | |
| html_content = """ | |
| <!DOCTYPE html> | |
| <html> | |
| <body> | |
| <div id="content"></div> | |
| <script> | |
| var source = new EventSource("https://matthoffner-starchat-alpha.hf.space/stream"); | |
| source.onmessage = function(event) { | |
| document.getElementById("content").innerHTML += event.data | |
| }; | |
| </script> | |
| </body> | |
| </html> | |
| """ | |
| return HTMLResponse(content=html_content, status_code=200) | |
| async def chat(prompt = "Write a simple express erver"): | |
| 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)) | |
| async def chat(request: ChatCompletionRequest, response_mode=None): | |
| tokens = llm.tokenize(request.prompt) | |
| async def server_sent_events(chat_chunks, llm): | |
| for token in llm.generate(chat_chunks): | |
| yield llm.detokenize(token) | |
| yield "" | |
| return EventSourceResponse(server_sent_events(tokens, llm)) | |
| if __name__ == "__main__": | |
| uvicorn.run(app, host="0.0.0.0", port=8000) | |