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from fastapi import FastAPI, Request |
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from fastapi.responses import StreamingResponse, JSONResponse |
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from pydantic import BaseModel, Field |
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from typing import List, Optional, Union |
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import requests |
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import json |
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app = FastAPI() |
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API_URL = "https://api.typegpt.net/v1/chat/completions" |
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API_KEY = "sk-XzS5hhsa3vpIcRLz3prQirBQXOx2hPydPzSpzdRcE1YddnNm" |
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BACKEND_MODEL = "pixtral-large-latest" |
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with open("model_map.json", "r") as f: |
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MODEL_PROMPTS = json.load(f) |
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class Message(BaseModel): |
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role: str |
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content: str |
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class ChatRequest(BaseModel): |
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model: str |
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messages: List[Message] |
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stream: Optional[bool] = False |
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temperature: Optional[float] = 1.0 |
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top_p: Optional[float] = 1.0 |
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n: Optional[int] = 1 |
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stop: Optional[Union[str, List[str]]] = None |
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presence_penalty: Optional[float] = 0.0 |
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frequency_penalty: Optional[float] = 0.0 |
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def build_payload(chat: ChatRequest): |
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system_prompt = MODEL_PROMPTS.get(chat.model, "You are a helpful assistant.") |
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messages = [{"role": "system", "content": system_prompt}] + [ |
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{"role": msg.role, "content": msg.content} for msg in chat.messages |
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] |
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return { |
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"model": BACKEND_MODEL, |
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"messages": messages, |
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"stream": chat.stream, |
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"temperature": chat.temperature, |
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"top_p": chat.top_p, |
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"n": chat.n, |
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"stop": chat.stop, |
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"presence_penalty": chat.presence_penalty, |
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"frequency_penalty": chat.frequency_penalty |
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} |
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def stream_generator(requested_model, payload, headers): |
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with requests.post(API_URL, headers=headers, json=payload, stream=True) as r: |
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for line in r.iter_lines(): |
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if line: |
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decoded = line.decode('utf-8') |
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if BACKEND_MODEL in decoded: |
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decoded = decoded.replace(BACKEND_MODEL, requested_model) |
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yield f"data: {decoded}\n\n" |
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yield "data: [DONE]\n\n" |
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@app.post("/v1/chat/completions") |
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async def proxy_chat(request: Request): |
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body = await request.json() |
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chat_request = ChatRequest(**body) |
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payload = build_payload(chat_request) |
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headers = { |
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"Authorization": f"Bearer {API_KEY}", |
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"Content-Type": "application/json" |
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} |
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if chat_request.stream: |
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return StreamingResponse( |
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stream_generator(chat_request.model, payload, headers), |
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media_type="text/event-stream" |
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) |
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else: |
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response = requests.post(API_URL, headers=headers, json=payload) |
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data = response.json() |
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if "model" in data and data["model"] == BACKEND_MODEL: |
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data["model"] = chat_request.model |
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return JSONResponse(content=data) |
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