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| import os | |
| from typing import Optional | |
| from fastapi import FastAPI | |
| from pydantic import BaseModel | |
| from huggingface_hub import login | |
| import requests | |
| API_URL = "https://router.huggingface.co/v1/chat/completions" | |
| headers = { | |
| "Authorization": f"Bearer {os.environ['HF_TOKEN']}", | |
| } | |
| # Login if token is available (e.g., via Hugging Face Spaces secrets) | |
| if "HF_TOKEN" in os.environ: | |
| login(token=os.environ["HF_TOKEN"]) | |
| app = FastAPI() | |
| class Prompt(BaseModel): | |
| text: str | |
| instructions: Optional[str] = None | |
| output_format: Optional[str] = None | |
| def query(payload): | |
| response = requests.post(API_URL, headers=headers, json=payload) | |
| return response.json() | |
| def read_root(): | |
| return {"Hello": "docker embeddings!"} | |
| async def generate_text(prompt: Prompt): | |
| messages = [] | |
| # Add system instructions if provided | |
| if prompt.instructions: | |
| messages.append({ | |
| "role": "system", | |
| "content": prompt.instructions | |
| }) | |
| # Add user message | |
| user_content = prompt.text | |
| # If an output format is specified, append formatting instructions | |
| if prompt.output_format: | |
| user_content += f"\n\nPlease always format the response as: {prompt.output_format}" | |
| messages.append({ | |
| "role": "user", | |
| "content": user_content | |
| }) | |
| # Call HF API | |
| response = query({ | |
| "messages": messages, | |
| "model": "meta-llama/Llama-3.2-3B-Instruct" | |
| }) | |
| return {"response": response["choices"][0]["message"]} | |