Spaces:
Sleeping
Sleeping
File size: 1,410 Bytes
1a9e80b 1e7060d 1a9e80b 1e7060d 1a9e80b 1e7060d 1a9e80b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 |
import numpy as np
from PIL import Image
import torch
from fastapi import FastAPI, UploadFile, File
from fastapi.responses import JSONResponse
from pydantic import BaseModel
app = FastAPI()
# Load the pre-trained model
model_uri = "model.pth"
model = torch.load(model_uri)
# Define input schema for JSON requests
class ImageInput(BaseModel):
image_path: str
# Preprocess the image
def preprocess_image(image):
image = image.convert('L') # Convert to grayscale
image = image.resize((28, 28))
image = np.array(image) / 255.0 # Normalize to [0, 1]
image = (image - 0.1307) / 0.3081 # Standardize
image = torch.tensor(image).unsqueeze(0).float() # Convert to tensor with batch dimension
return image
# Root endpoint
@app.get("/")
def greet_json():
return {"Hello": "World!"}
# Predict endpoint for JSON input
@app.post("/predict")
async def predict_image(file: UploadFile = File(...)):
try:
# Read and preprocess the uploaded image
image = Image.open(file.file)
image = preprocess_image(image)
# Make prediction
model.eval()
with torch.no_grad():
output = model(image)
prediction = output.argmax(dim=1).item()
return JSONResponse(content={"prediction": f"The digit is {prediction}"})
except Exception as e:
return JSONResponse(content={"error": str(e)}, status_code=500) |