# -*- coding: utf-8 -*-
"""app.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1AAv25Dmp5rMgoK7FgBmpAip9IZCt1KFK
"""

from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel

# FastAPI instance initialization
app = FastAPI()

# Allowing CORS to let Streamlit access the API
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Allow any origin
    allow_methods=["*"],  # Allow any method
    allow_headers=["*"],  # Allow any headers
)

# Request model for task input
class TaskRequest(BaseModel):
    task: str

@app.post("/predict")
def predict_model(req: TaskRequest):
    # Example logic: differentiate between regression and classification tasks
    if "price" in req.task.lower():
        return {
            "model": "Linear Regression",
            "r_squared": 0.82,
            "coefficients": {"area": 2.3, "bedrooms": -0.7}
        }
    else:
        return {
            "model": "Logistic Regression",
            "accuracy": 0.78,
            "coefficients": {"feature1": 1.1, "feature2": -0.3}
        }

# Start FastAPI app on Hugging Face Space
if __name__ == "__main__":
    import uvicorn
    uvicorn.run("app:app", host="0.0.0.0", port=7860)