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from fastapi import FastAPI, UploadFile
from PIL import Image
from fastapi.responses import FileResponse
from fastapi.staticfiles import StaticFiles
from pathlib import Path
import mnist_classifier
import torch
from pathlib import Path
import datetime
import numpy as np
from pydantic import BaseModel
app = FastAPI()
app.mount("/static", StaticFiles(directory=Path("static")), name="static")
@app.get("/")
async def root():
return FileResponse("static/index.html")
upload_dir = Path("uploads")
upload_dir.mkdir(parents=True, exist_ok=True)
def process_image(file):
image = Image.open(file.file)
image = image.resize((28, 28)) # Resize to MNIST image size
image = image.convert("L") # Convert to grayscale
raw_image = image
image = np.array(image)
image = image / 255.0 # Normalize pixel values
return torch.from_numpy(image).float().reshape(1, 28, 28), raw_image
def store_img(image):
timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
unique_filename = f"{timestamp}.png"
output_path = upload_dir / unique_filename
image.save(output_path)
@app.post("/predict")
async def predict(image: UploadFile):
return {'msg': 'ok'}
print(image)
# tensor_image, raw_image = process_image(image)
# print(tensor_image.shape)
# prediction = mnist_classifier.predict(tensor_image)
# store_img(raw_image)
prediction = []
return {"prediction": prediction} |