Application Code Added

Dockerfile

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+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . /code
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

alzheimer_weight.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:854d74d38ba0ba6d4adb8085063cea702f96ab4a76171a759137d4e467cfc0c4
+size 16351531

app.py

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+import numpy as np
+from PIL import Image
+import cv2
+from fastapi import FastAPI, UploadFile, File
+from utils import classify_img,get_alzheimer_model
+import json
+from io import BytesIO
+import torch
+import torchvision
+
+app=FastAPI(title="Alzheimer Detection API")
+
+@app.get("/")
+def message():
+    return "Welcome"
+
+@app.post("/predict")
+def predict(file: UploadFile):
+    img=Image.open(file.file).convert("RGB")
+    img=img.resize(480,480)
+    img=np.array(img)
+    model= get_alzheimer_model()
+    label,probability=classify_img(model,img)
+    return {"label":label.item(),"probability":probability.item()}

requirements.txt

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+numpy==1.25.2
+torch==2.0.1
+fastapi==0.104.1
+uvicorn==0.24.0.post1
+python-multipart==0.0.6
+torchvision==0.15.2
+opencv-python-headless==4.5.4.60
+numpy==1.25.2
+streamlit==1.26.0

utils.py

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+import numpy as np
+import torch
+import torch.nn as nn
+from torchvision.models import efficientnet_b0
+import torchvision.transforms.functional as tf
+
+def classify_img(model,img):
+    img=tf.to_tensor(img)
+    img=img.unsqueeze(0)
+    with torch.no_grad():
+        predict=model(img)
+        predict=nn.functional.softmax(predict,1)
+        label=torch.argmax(predict)
+        probability=torch.max(predict)
+        return label,probability
+
+def get_alzheimer_model():
+    model=efficientnet_b0(weights=None)
+    in_features=model.classifier[1].in_features
+    model.classifier[1]=nn.Linear(in_features=in_features,out_features=4)
+    weights=torch.load("alzheimer_weight.pth",map_location="cpu")
+    model.load_state_dict(weights)
+    model.eval()
+    return model