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main.py
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main.py
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import os
import torch
from fastapi import FastAPI, UploadFile, File, Request
from fastapi.responses import HTMLResponse
from fastapi.staticfiles import StaticFiles
from starlette.templating import Jinja2Templates
from model_utils import predict_image
import imghdr
app = FastAPI()
static_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "static")
app.mount("/static", StaticFiles(directory=static_path), name="static")
model = torch.jit.load(
'scripted_model.pt',
map_location='cpu'
)
model.eval()
templates = Jinja2Templates(directory="templates")
@app.get("/", response_class=HTMLResponse)
async def home(request: Request):
return templates.TemplateResponse("alzheimer.html", {"request": request})
@app.post("/predict")
async def predict(request: Request, img: UploadFile = File(...)):
if img.filename == "":
return templates.TemplateResponse(
"alzheimer.html",
{"request": request, "prediction": "No file selected."}
)
# Check if the file is an image
file_extension = imghdr.what(img.file)
if not file_extension:
return templates.TemplateResponse(
"alzheimer.html",
{"request": request, "prediction": "Invalid file format. Please upload an image."}
)
image_bytes = await img.read()
prediction = predict_image(model, image_bytes)
return templates.TemplateResponse(
"alzheimer.html",
{"request": request, "prediction": prediction}
)