TP4

TP3/src/main.cpp

@@ -1,69 +1,66 @@
-
 #include "DHT.h"
+
 #define DHTPIN 2      // Digital pin connected to the DHT sensor
-#define DHTTYPE DHT22 // DHT 22  (AM2302), AM2321
+#define DHTTYPE DHT22 // DHT 22 (AM2302), AM2321
 DHT dht(DHTPIN, DHTTYPE);
 
-const int N_FEATURES = 12;
-const float MEAN[N_FEATURES] = {/* μ_Temperature, μ_Humidity */};
-const float STD[N_FEATURES] = {/* σ_Temperature, σ_Humidity */};
-const float WEIGHTS[N_FEATURES] = {/* W_Temperature, W_Humidity */};
-const float BIAS = 0; /* b */
+const int N_FEATURES = 2; // فقط الحرارة والرطوبة في المثال
+
+// إحصائيات البيانات (قيم تجريبية افتراضية)
+const float MEAN[N_FEATURES] = {25.0, 60.0}; // المتوسط
+const float STD[N_FEATURES] = {5.0, 10.0};   // الانحراف المعياري
 
-float X[N_FEATURES] = {20.0, 57.36, 0, 400, 12306, 18520, 939.735, 0.0, 0.0, 0.0, 0.0, 0.0}; // Input features
+// الأوزان والانحياز (افتراضية كمثال)
+const float WEIGHTS[N_FEATURES] = {0.3, -0.2};
+const float BIAS = 0.5;
+
+float X[N_FEATURES];
 
 void setup()
 {
   Serial.begin(9600);
-  Serial.println(F("DHTxx test!"));
+  Serial.println(F("DHT22 test + ML model!"));
   dht.begin();
 }
 
 void loop()
 {
   delay(2000);
 
-  // Reading temperature or humidity takes about 250 milliseconds!
-  // Sensor readings may also be up to 2 seconds 'old' (its a very slow sensor)
   float h = dht.readHumidity();
-  // Read temperature as Celsius (the default)
   float t = dht.readTemperature();
-  // Read temperature as Fahrenheit (isFahrenheit = true)
-  float f = dht.readTemperature(true);
 
-  // add data to input array
-  X[0] = t;
-  X[1] = h;
-
-  // Check if any reads failed and exit early (to try again).
-  if (isnan(h) || isnan(t) || isnan(f))
+  if (isnan(h) || isnan(t))
   {
     Serial.println(F("Failed to read from DHT sensor!"));
     return;
   }
 
-  // TODO: Add code to standardize the inputs
-
-  // TODO: Add code to compute the output of wx + b
+  X[0] = t;
+  X[1] = h;
 
-  // TODO: Add code to apply the sigmoid function
+  // 1️⃣ توحيد القيم (Standardization)
+  float X_std[N_FEATURES];
+  for (int i = 0; i < N_FEATURES; i++)
+  {
+    X_std[i] = (X[i] - MEAN[i]) / STD[i];
+  }
 
-  // TODO: Add code to print the result to the serial monitor
+  // 2️⃣ حساب wx + b
+  float linear = BIAS;
+  for (int i = 0; i < N_FEATURES; i++)
+  {
+    linear += WEIGHTS[i] * X_std[i];
+  }
 
-  // Compute heat index in Fahrenheit (the default)
-  // float hif = dht.computeHeatIndex(f, h);
-  // Compute heat index in Celsius (isFahreheit = false)
-  // float hic = dht.computeHeatIndex(t, h, false);
+  // 3️⃣ تطبيق دالة sigmoid
+  float sigmoid = 1.0 / (1.0 + exp(-linear));
 
-  Serial.print("Humidity: ");
-  Serial.print(h);
-  Serial.print("%  Tempeature: ");
+  // 4️⃣ طباعة النتائج
+  Serial.print("Temp: ");
   Serial.print(t);
-  Serial.print("°C ");
-  Serial.println(f);
-  // Serial.print(F("°F  Heat index: "));
-  // Serial.print(hic);
-  // Serial.print(F("°C "));
-  // Serial.print(hif);
-  // Serial.println(F("°F"));
-}
+  Serial.print("°C  Humidity: ");
+  Serial.print(h);
+  Serial.print("%  -> Model Output: ");
+  Serial.println(sigmoid, 4); // 4 منازل عشرية
+}

TP4/ai_logic/mqtt_ai_subscriber.py

@@ -1,15 +1,79 @@
-import json, paho.mqtt.client as mqtt
+import json
+import pickle
+import paho.mqtt.client as mqtt
 
-def on_message(client, userdata, msg):
-    data = json.loads(msg.payload)
-    print(f"Received: {data}")
-    if data["temperature"] > 30:
-        client.publish("esp32/control", "ON")
+# === تحميل النموذجين ===
+with open("../models/model_lr.pkl", "rb") as f:
+    model_lr = pickle.load(f)
+
+with open("../models/model_xgb.pkl", "rb") as f:
+    model_xgb = pickle.load(f)
+
+
+# النموذج الحالي المستخدم
+current_model = "lr"
+models = {"lr": model_lr, "xgb": model_xgb}
+
+print("✅ Models loaded successfully (LR + XGB)")
+print("🧠 Current model:", current_model)
+
+# === دالة التبديل بين النماذج ===
+def switch_model(new_model):
+    global current_model
+    if new_model in models:
+        current_model = new_model
+        print(f"🔁 Model switched to: {new_model}")
     else:
-        client.publish("esp32/control", "OFF")
+        print(f"⚠️ Unknown model: {new_model}")
+
+# === عند استقبال رسالة ===
+def on_message(client, userdata, msg):
+    global current_model
+
+    topic = msg.topic
+    payload = msg.payload.decode("utf-8")
+
+    # إذا كانت رسالة التحكم بالنموذج
+    if topic == "esp32/model":
+        switch_model(payload.strip())
+        return
+
+    # رسائل البيانات من Arduino
+    if topic == "esp32/data":
+        try:
+            data = json.loads(payload)
+            temp = data.get("temperature")
+            humid = data.get("humidity")
 
+            if temp is None or humid is None:
+                print("⚠️ Missing temperature/humidity in data.")
+                return
+
+            # تجهيز الإدخال للنموذج
+            X = [[temp, humid]]
+            model = models[current_model]
+            pred = model.predict(X)[0]
+
+            # إرسال القرار إلى ESP32
+            command = "ON" if pred == 1 else "OFF"
+            print(f"📩 Data: T={temp:.1f}°C, H={humid:.1f}% → Model={current_model} → {command}")
+            client.publish("esp32/control", command)
+
+        except Exception as e:
+            print("❌ Error handling message:", e)
+
+# === إعداد MQTT ===
 client = mqtt.Client()
-client.connect("broker.mqtt.cool", 1883, 60)
-client.subscribe("esp32/data")
 client.on_message = on_message
+
+broker = "broker.mqtt.cool"
+port = 1883
+
+client.connect(broker, port, 60)
+client.subscribe("esp32/data")
+client.subscribe("esp32/model")  # لتبديل النموذج
+
+print(f"🚀 Connected to MQTT broker at {broker}:{port}")
+print("📡 Listening for data...")
+
 client.loop_forever()

TP4/createmodel.py

@@ -0,0 +1,47 @@
+# === create_models.py ===
+import os
+import pickle
+import numpy as np
+from sklearn.linear_model import LogisticRegression
+from xgboost import XGBClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+# إنشاء مجلد models إذا لم يكن موجودًا
+os.makedirs("models", exist_ok=True)
+
+# توليد بيانات تجريبية (حرارة ورطوبة)
+# الهدف هنا تصنيف حالة الطقس مثلاً (0 = عادي، 1 = خطر)
+np.random.seed(42)
+temp = np.random.uniform(15, 40, 200)   # حرارة بين 15 و40
+humid = np.random.uniform(20, 90, 200)  # رطوبة بين 20 و90
+labels = (temp > 30) & (humid > 60)     # إذا الحرارة >30 والرطوبة >60 = خطر
+labels = labels.astype(int)
+
+X = np.column_stack((temp, humid))
+y = labels
+
+# تقسيم البيانات
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# --- نموذج Logistic Regression ---
+model_lr = LogisticRegression()
+model_lr.fit(X_train, y_train)
+y_pred_lr = model_lr.predict(X_test)
+print("Logistic Regression Accuracy:", accuracy_score(y_test, y_pred_lr))
+
+# حفظ النموذج
+with open("models/model_lr.pkl", "wb") as f:
+    pickle.dump(model_lr, f)
+
+# --- نموذج XGBoost ---
+model_xgb = XGBClassifier(use_label_encoder=False, eval_metric='logloss')
+model_xgb.fit(X_train, y_train)
+y_pred_xgb = model_xgb.predict(X_test)
+print("XGBoost Accuracy:", accuracy_score(y_test, y_pred_xgb))
+
+# حفظ النموذج
+with open("models/model_xgb.pkl", "wb") as f:
+    pickle.dump(model_xgb, f)
+
+print("\n✅ تم إنشاء النموذجين model_lr.pkl و model_xgb.pkl داخل مجلد models")