Instructions to use SudhanvaD/FinTrack-Stock-LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use SudhanvaD/FinTrack-Stock-LLM with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("<base-model-id>") model.load_adapter("SudhanvaD/FinTrack-Stock-LLM", set_active=True) - Notebooks
- Google Colab
- Kaggle
Download backend/server.py from SudhanvaD/FinTrack-Stock-LLM: direct link, hf CLI and curl.
- Browser
- Download file 867 Bytes
-
https://huggingface.co/SudhanvaD/FinTrack-Stock-LLM/resolve/main/backend/server.py
- Command line
-
hf download hf://SudhanvaD/FinTrack-Stock-LLM/backend/server.py
-
curl -L -o server.py https://huggingface.co/SudhanvaD/FinTrack-Stock-LLM/resolve/main/backend/server.py
867 Bytes
| from fastapi import FastAPI | |
| from fastapi.middleware.cors import CORSMiddleware | |
| from pydantic import BaseModel | |
| from gemini import generate_answer | |
| app = FastAPI() | |
| # ✅ Allow requests from your frontend URL | |
| app.add_middleware( | |
| CORSMiddleware, | |
| allow_origins=[ | |
| "https://fintrack-flax.vercel.app", # Vercel deployment URL | |
| "http://localhost:5173", # Vite default port | |
| "http://127.0.0.1:5173", # Some systems resolve as 127.0.0.1 instead | |
| ], | |
| allow_credentials=True, | |
| allow_methods=["*"], | |
| allow_headers=["*"], | |
| ) | |
| class StrategyRequest(BaseModel): | |
| user_input: str | |
| async def generate_strategy(request: StrategyRequest): | |
| try: | |
| response = generate_answer(request.user_input) | |
| return {"strategy_code": response} | |
| except Exception as e: | |
| return {"error": str(e)} | |