Create app.py

app.py

@@ -0,0 +1,38 @@
+import os
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.models import load_model
+from PIL import Image
+from flask import Flask, request, jsonify
+
+app = Flask(__name__)
+model = None
+class_dict = None
+
+def load_saved_model():
+    global model
+    global class_dict
+    model = load_model("path/to/your/saved/model.h5")  # Update with the actual path to your saved model
+    class_dict = {0: "Class 1", 1: "Class 2", 2: "Class 3"}  # Update with the actual class names
+
+@app.route("/predict", methods=["POST"])
+def predict():
+    if "image" not in request.files:
+        return jsonify({"error": "No image found in the request."}), 400
+    
+    image = request.files["image"]
+    image = Image.open(image)
+    image = image.resize((75, 75))
+    image = np.array(image) / 255.0
+    image = np.expand_dims(image, axis=0)
+    
+    prediction = model.predict(image)
+    class_id = np.argmax(prediction)
+    class_name = class_dict.get(class_id, "Unknown")
+    
+    return jsonify({"class_id": class_id, "class_name": class_name}), 200
+
+if __name__ == "__main__":
+    load_saved_model()
+    app.run()