from fastapi import FastAPI
from pydantic import BaseModel
import numpy as np
from tensorflow import keras
import os
from fastapi.middleware.cors import CORSMiddleware
app = FastAPI()



origins = [
    "https://namanrajput-git.github.io/RT_Digit_Recognizer-Frontend/", 
 
]

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=False,
    allow_methods=["*"],
    allow_headers=["*"],
)


print("Current working directory:", os.getcwd())
print("Files in app folder:", os.listdir("."))


model_path = "best_model.h5"
model = keras.models.load_model(model_path)
class InputData(BaseModel):
    pixels: list 

@app.get("/")
def root():
    return {"message": "MNIST API running"}

@app.post("/predict")
def predict(data: InputData):
    X = np.array(data.pixels).reshape(1, 28, 28, 1) / 255.0
    y_pred = model.predict(X)
    predicted_class = int(np.argmax(y_pred, axis=1)[0])
    return {"prediction": predicted_class}