main.py

import uvicorn
import json

from ctransformers import AutoModelForCausalLM
from fastapi import FastAPI, Form
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
from typing import List, Dict
from fastapi.middleware.cors import CORSMiddleware

#Model loading
model = AutoModelForCausalLM.from_pretrained("TheBloke/Vigostral-7B-Chat-GGUF",
    model_file="vigostral-7b-chat.Q4_K_M.gguf",
    model_type="mistral",
    threads = 3
)

#Fast API
app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins = ["*"],
    allow_credentials = True,
    allow_methods = ["*"],
    allow_headers = ["*"],
)

def apply_chat_template(conversation: List):
    formatted_conversation = ""

    for turn in conversation:
        role = turn.role.upper()
        content = turn.content
        if role == "SYSTEM":
            formatted_conversation += "<s>[INST] <<SYS>>\n" + content + "\n<</SYS>>"
        elif role == "ASSISTANT":
            formatted_conversation += "\n[/INST] " + content + " </s>"
        else:
            formatted_conversation += "[INST] " + content + " [/INST]"

    return formatted_conversation

#Pydantic object
class Message(BaseModel):
    role: str
    content: str

class Validation(BaseModel):
    messages: List[Message]
    model: str
    temperature: float
    presence_penalty: float
    top_p: float
    frequency_penalty: float
    stream: bool

@app.post("/chat")
async def stream(item: Validation):

    prompt = apply_chat_template(item.messages)

    def stream_json():
        for text in model(
            prompt,
            temperature=item.temperature, # Default to 0.8
            top_p=item.top_p, # Default to 0.95
            repetition_penalty=item.frequency_penalty, # Default to 1.1
            stream=True
            ):
            yield json.dumps({
                "object":"chat.completion.chunk",
                "choices": [
                    {
                        "index": 0,
                        "delta" : {
                            "content": text
                        }
                    }
                ]
            })

    return StreamingResponse(stream_json(), media_type="application/json")

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)