from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import FileResponse
from pydantic import BaseModel
import pickle
import os

# -------------------------
# Load ML model
# -------------------------
model_path = os.path.join(os.path.dirname(__file__), "student_model.pkl")
with open(model_path, "rb") as f:
    model = pickle.load(f)

# -------------------------
# FastAPI app setup
# -------------------------
app = FastAPI(title="Student Pass/Fail Predictor")

# Enable CORS so JS can call API
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"]
)

# -------------------------
# Serve HTML page at root
# -------------------------
@app.get("/")
def home():
    return FileResponse("index.html")

# -------------------------
# Input schema
# -------------------------
class StudentData(BaseModel):
    Math: float
    English: float
    Science: float

# -------------------------
# Prediction endpoint
# -------------------------
@app.post("/predict")
def predict(data: StudentData):
    features = [[data.Math, data.English, data.Science]]
    prediction = model.predict(features)[0]
    result = "Pass" if prediction == 1 else "Fail"
    return {"result": result}