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| import os | |
| from fastapi import FastAPI | |
| from pydantic import BaseModel | |
| from huggingface_hub import InferenceClient | |
| import uvicorn | |
| app = FastAPI() | |
| # ✅ CHANGE: Get API token from Hugging Face Spaces secret instead of .env | |
| HF_TOKEN = os.getenv("HF_TOKEN") | |
| if not HF_TOKEN: | |
| raise ValueError("Hugging Face API token (HF_TOKEN) is missing!") | |
| # ✅ CHANGE: Pass the token to InferenceClient | |
| client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1", token=HF_TOKEN) | |
| class Item(BaseModel): | |
| prompt: str | |
| history: list | |
| system_prompt: str | |
| temperature: float = 0.0 | |
| max_new_tokens: int = 1048 | |
| top_p: float = 0.15 | |
| repetition_penalty: float = 1.0 | |
| def format_prompt(message, history): | |
| prompt = "<s>" | |
| for user_prompt, bot_response in history: | |
| prompt += f"[INST] {user_prompt} [/INST] {bot_response}</s> " | |
| prompt += f"[INST] {message} [/INST]" | |
| return prompt | |
| def generate(item: Item): | |
| temperature = max(float(item.temperature), 1e-2) | |
| top_p = float(item.top_p) | |
| generate_kwargs = { | |
| "temperature": temperature, | |
| "max_new_tokens": item.max_new_tokens, | |
| "top_p": top_p, | |
| "repetition_penalty": item.repetition_penalty, | |
| "do_sample": True, | |
| "seed": 42, | |
| } | |
| formatted_prompt = format_prompt(f"{item.system_prompt}, {item.prompt}", item.history) | |
| # ✅ CHANGE: Use token authentication for API call | |
| stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False) | |
| output = "".join(response.token.text for response in stream) | |
| return output | |
| async def generate_text(item: Item): | |
| return {"response": generate(item)} | |
| # ✅ ADDING TEST API ENDPOINT | |
| async def test_api(): | |
| return {"message": "API is working!"} | |
| # ✅ ADDING MAIN CHECK FOR LOCAL TESTING | |
| if __name__ == "__main__": | |
| uvicorn.run(app, host="0.0.0.0", port=7860) | |