Update app.py

app.py

@@ -1,101 +1,107 @@
-from flask import Flask, request, render_template
-import joblib
-from tensorflow.keras.models import load_model
-import numpy as np
-import os
-
-# Load models
-model_load = load_model('lstm_model.h5')
-scaler_load = joblib.load('scaler.sav')  # Load the correct scaler
-
-# Flask app initialization
-app = Flask(__name__)
-app.config["SECRET_KEY"] = os.urandom(24)
-app.config["DEBUG"] = True
-
-def validasi_inputan(form_data):
-    errors = {}
-
-    # Validasi temp_1, temp_2, temp_3
-    for field in ["temp_1", "temp_2", "temp_3"]:
-        if not form_data.get(field):
-            errors[field] = f"{field} tidak boleh kosong."
-        else:
-            try:
-                float(form_data.get(field))
-            except ValueError:
-                errors[field] = f"{field} harus berupa angka."
-
-    # Validasi feelslike_1, feelslike_2, feelslike_3
-    for field in ["feelslike_1", "feelslike_2", "feelslike_3"]:
-        if not form_data.get(field):
-            errors[field] = f"{field} tidak boleh kosong."
-        else:
-            try:
-                float(form_data.get(field))
-            except ValueError:
-                errors[field] = f"{field} harus berupa angka."
-
-    return errors
-
-def validate_data(record):
-    errors = {}
-
-    # Validasi rentang nilai (15 - 50 untuk suhu)
-    for key, value in record.items():
-        if value < 15 or value > 50:
-            errors[key] = f"{key} harus diantara 15 dan 50."
-
-    return errors
-
-@app.route("/", methods=["GET", "POST"])
-def index():
-    prediction = None
-    errors = {}
-    predictions_list = []
-
-    if request.method == "POST":
-        # Validasi input kosong
-        errors = validasi_inputan(request.form)
-
-        if not errors:
-            record = {
-                "temp_1": float(request.form.get("temp_1")),
-                "temp_2": float(request.form.get("temp_2")),
-                "temp_3": float(request.form.get("temp_3")),
-                "feelslike_1": float(request.form.get("feelslike_1")),
-                "feelslike_2": float(request.form.get("feelslike_2")),
-                "feelslike_3": float(request.form.get("feelslike_3")),
-            }
-
-            # Validasi rentang nilai
-            errors = validate_data(record)
-
-            if not errors:
-                # Input data untuk prediksi
-                input_data = np.array([
-                    [record["temp_1"], record["feelslike_1"]],
-                    [record["temp_2"], record["feelslike_2"]],
-                    [record["temp_3"], record["feelslike_3"]]
-                ])
-
-                # Scaling input data
-                input_data_scaled = scaler_load.transform(input_data)
-
-                # Prediksi untuk 5 periode ke depan
-                last_input_scaled = input_data_scaled.copy()
-
-                for _ in range(5):  # Prediksi 5 periode
-                    prediction_normalized = model_load.predict(last_input_scaled.reshape(1, 3, 2))
-                    prediction_denormalized = scaler_load.inverse_transform(prediction_normalized)
-                    predictions_list.append(prediction_denormalized.flatten())
-
-                    # Update input dengan prediksi terbaru
-                    last_input_scaled = np.append(last_input_scaled[1:], prediction_normalized, axis=0)
-
-
-
-    return render_template('index.html', prediction=predictions_list, errors=errors, record=request.form)
-
-if __name__ == "__main__":
-    app.run()
+from fastapi import FastAPI
+from asgiref.wsgi import WsgiToAsgi
+from flask import Flask, request, render_template
+import joblib
+from tensorflow.keras.models import load_model
+import numpy as np
+import os
+from fastapi.middleware.wsgi import WSGIMiddleware
+
+# Load models
+model_load = load_model('lstm_model.h5')
+scaler_load = joblib.load('scaler.sav')  # Load the correct scaler
+
+# Flask app initialization
+app = Flask(__name__)
+app.config["SECRET_KEY"] = os.urandom(24)
+app.config["DEBUG"] = True
+
+def validasi_inputan(form_data):
+    errors = {}
+
+    # Validasi temp_1, temp_2, temp_3
+    for field in ["temp_1", "temp_2", "temp_3"]:
+        if not form_data.get(field):
+            errors[field] = f"{field} tidak boleh kosong."
+        else:
+            try:
+                float(form_data.get(field))
+            except ValueError:
+                errors[field] = f"{field} harus berupa angka."
+
+    # Validasi feelslike_1, feelslike_2, feelslike_3
+    for field in ["feelslike_1", "feelslike_2", "feelslike_3"]:
+        if not form_data.get(field):
+            errors[field] = f"{field} tidak boleh kosong."
+        else:
+            try:
+                float(form_data.get(field))
+            except ValueError:
+                errors[field] = f"{field} harus berupa angka."
+
+    return errors
+
+def validate_data(record):
+    errors = {}
+
+    # Validasi rentang nilai (15 - 50 untuk suhu)
+    for key, value in record.items():
+        if value < 15 or value > 50:
+            errors[key] = f"{key} harus diantara 15 dan 50."
+
+    return errors
+
+@app.route("/", methods=["GET", "POST"])
+def index():
+    prediction = None
+    errors = {}
+    predictions_list = []
+
+    if request.method == "POST":
+        # Validasi input kosong
+        errors = validasi_inputan(request.form)
+
+        if not errors:
+            record = {
+                "temp_1": float(request.form.get("temp_1")),
+                "temp_2": float(request.form.get("temp_2")),
+                "temp_3": float(request.form.get("temp_3")),
+                "feelslike_1": float(request.form.get("feelslike_1")),
+                "feelslike_2": float(request.form.get("feelslike_2")),
+                "feelslike_3": float(request.form.get("feelslike_3")),
+            }
+
+            # Validasi rentang nilai
+            errors = validate_data(record)
+
+            if not errors:
+                # Input data untuk prediksi
+                input_data = np.array([
+                    [record["temp_1"], record["feelslike_1"]],
+                    [record["temp_2"], record["feelslike_2"]],
+                    [record["temp_3"], record["feelslike_3"]]
+                ])
+
+                # Scaling input data
+                input_data_scaled = scaler_load.transform(input_data)
+
+                # Prediksi untuk 5 periode ke depan
+                last_input_scaled = input_data_scaled.copy()
+
+                for _ in range(5):  # Prediksi 5 periode
+                    prediction_normalized = model_load.predict(last_input_scaled.reshape(1, 3, 2))
+                    prediction_denormalized = scaler_load.inverse_transform(prediction_normalized)
+                    predictions_list.append(prediction_denormalized.flatten())
+
+                    # Update input dengan prediksi terbaru
+                    last_input_scaled = np.append(last_input_scaled[1:], prediction_normalized, axis=0)
+
+
+
+    return render_template('index.html', prediction=predictions_list, errors=errors, record=request.form)
+
+# FastAPI app to mount Flask app
+app = FastAPI()
+
+# Mount Flask app inside FastAPI using WSGIMiddleware
+app.mount("/", WSGIMiddleware(app))