Added nn6

06_NN/code/nn_6_flask_app.py

@@ -0,0 +1,70 @@
+import io
+import torch
+import torch.nn as nn
+import torchvision.models as models
+import torchvision.transforms as transforms
+from PIL import Image
+from flask import Flask, request, jsonify
+import os
+import json
+import urllib.request
+
+
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('mps') if torch.backends.mps.is_available() else torch.device('cpu')
+print(device)
+
+model = models.resnet18(weights = models.ResNet18_Weights.IMAGENET1K_V1)
+model.eval()
+model.to(device)
+
+transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=[0.485, 0.456, 0.406],  # ImageNet Normalisierung
+        std=[0.229, 0.224, 0.225]
+    )
+])
+
+app = Flask(__name__)
+
+@app.route("/predict", methods=["POST"])
+def predict():
+    """
+    Erwartet eine Bilddatei ('image') im POST-Request.
+    Beispiel (Postman): POST -> http://127.0.0.1:5665/predict
+    Body -> form-data -> key='image', value=<Bilddatei>
+    """
+    if "image" not in request.files:
+        return jsonify({"error": "Kein Bild hochgeladen!"}), 400
+
+    file = request.files["image"]
+    img_bytes = file.read()
+
+    try:
+        image = Image.open(io.BytesIO(img_bytes)).convert("RGB")
+    except Exception as e:
+        return jsonify({"error": f"Fehler beim Öffnen des Bildes: {str(e)}"}), 400
+
+    # Preprocessing
+    input_tensor = transform(image).unsqueeze(0).to(device)
+
+    # Inferenz
+    with torch.no_grad():
+        outputs = model(input_tensor)
+        _, predicted = outputs.max(1)
+
+    # Ergebnis zurückgeben
+    return jsonify({
+        "predicted_class_index": predicted.item()
+    })
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=5665, debug=True)
+
+
+
+
+
+