from fastapi import FastAPI
from llama_index_llms_huggingface_api import HuggingFaceInferenceAPI
from pydantic import BaseModel

# Define your Pydantic model with protected namespaces disabled
class ModelConfig(BaseModel):
    model_id: str
    model_name: str

    class Config:
        protected_namespaces = ()  # Disable protected namespaces warning

# Define Settings class
class Settings:
    llm = None  # Initialize the llm attribute

# Initialize FastAPI app
app = FastAPI()

# Set up the HuggingFaceInferenceAPI with the new class
Settings.llm = HuggingFaceInferenceAPI(
    api_key="your_api_key",  # Replace with your actual API key
    model_name="your_model_name"  # Replace with your actual model name
)

# Sample endpoint to test the LLM
@app.post("/predict/")
async def predict(input_text: str):
    try:
        response = Settings.llm.generate(input_text)  # Replace with the appropriate method for your API
        return {"prediction": response}
    except Exception as e:
        return {"error": str(e)}

# Sample endpoint to retrieve model configuration
@app.get("/model-config/")
async def get_model_config():
    return {"model_id": Settings.llm.model_id, "model_name": Settings.llm.model_name}

# Run the app using: uvicorn app:app --reload