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  Question CQ6: ESP32 Serial Multi-Sensor Stream & Real-Time ML Classifier [30 Marks]
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FULL QUESTION:
CAE 2 IoT Practical Exam - Combined Question:
CQ6. ESP32 Hardware Sensor Telemetry and Real-Time Decision Tree Room Occupancy Classification [30 Marks]
Develop an embedded hardware-to-software IoT pipeline using ESP32 microcontroller, physical/simulated DHT22 and LDR sensors, USB Serial communication, and a Decision Tree Classifier.
Hardware / Sensor Setup:
- ESP32 Microcontroller board
- DHT22 Temperature & Humidity Sensor connected to GPIO 4
- LDR Light Sensor connected to Analog ADC Pin GPIO 34
Communication: USB Serial Monitor at 115200 Baud transmitting formatted JSON packets.
Historical Dataset: UCI Room Occupancy Detection dataset (datatraining.txt).
Target Variable: Room Occupancy (0 = Vacant, 1 = Occupied).
The system should:
- Write an ESP32 Arduino C++ firmware sketch to read DHT22 (Temperature & Humidity) and LDR (Analog Light), format readings into a JSON string, and transmit via Serial at 115200 baud every 2 seconds. [8 Marks]
- Develop a Python Serial listener using pyserial (with automatic COM port detection and simulated fallback) to read and parse the live sensor stream. [6 Marks]
- Train a Decision Tree Classifier on historical datatraining.txt dataset to predict room occupancy based on Temperature, Humidity, and Light. Evaluate Accuracy Score and Confusion Matrix. [5 Marks]
- Perform real-time inferencing on every incoming serial packet and classify the room occupancy state. [5 Marks]
- Develop a Streamlit web dashboard displaying live sensor stream charts, connection status, dynamic occupancy status alert, and manual sensor sliders. [6 Marks]
Flow: ESP32 Hardware (DHT22 + LDR) -> USB Serial (115200) -> Python Serial Listener -> Decision Tree Model -> Streamlit Dashboard.

SHORT QUESTION:
ESP32 DHT22 & LDR Serial Stream with Decision Tree Occupancy Classifier and Streamlit Dashboard [30 Marks].

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README & SETUP INSTRUCTIONS:
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# Question CQ6: ESP32 Serial Multi-Sensor Stream & Decision Tree Classifier

## Problem Statement [30 Marks]
Interface an ESP32 microcontroller with a DHT22 sensor and an LDR sensor, transmit data over USB Serial at 115200 baud, and run real-time Decision Tree inference in Python.

- **Hardware**: ESP32, DHT22 (GPIO 4), LDR (GPIO 34 ADC)
- **Baud Rate**: 115200
- **Dataset**: `datatraining.txt` (or built-in fallback)
- **Model**: Decision Tree Classifier (`scikit-learn`)

---

## Architectural Flow
```text
ESP32 (DHT22 + LDR) ──(USB Serial: 115200)──> Python Serial Listener ──> Decision Tree ──> Streamlit UI
```

---

## Files in this Package
- `esp32_sensors.ino`: Arduino C++ sketch for ESP32 reading DHT22 and LDR, outputting formatted JSON over Serial.
- `serial_classifier.py`: Standalone CLI script reading COM port (or simulating serial input), training Decision Tree, and logging predictions.
- `app.py`: Streamlit real-time dashboard featuring live serial stream charts and occupancy alert indicators.

---

## How to Run

### Step 1: Install Dependencies
```bash
pip install pyserial pandas scikit-learn streamlit matplotlib
```

### Step 2: Upload Firmware to ESP32 (If Physical Board Available)
Open Arduino IDE, select board `DOIT ESP32 DEVKIT V1`, port, and upload `esp32_sensors.ino`.

### Step 3: Run the CLI Classifier Pipeline
```bash
python serial_classifier.py
```
*(Note: If no ESP32 is plugged in, the script automatically activates a realistic software serial simulator so your pipeline runs flawlessly!)*

### Step 4: Run Streamlit Web Dashboard
```bash
streamlit run app.py
```

================================================================================
SOURCE CODE FILES:
================================================================================

1. esp32_sensors.ino
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#include "DHT.h"

#define DHTPIN 4
#define DHTTYPE DHT22
#define LDRPIN 34

DHT dht(DHTPIN, DHTTYPE);

void setup() {
  Serial.begin(115200);
  delay(1000);
  dht.begin();
  pinMode(LDRPIN, INPUT);

  Serial.println("==================================================");
  Serial.println("  ESP32 DHT22 + LDR Telemetry Transmitter Active");
  Serial.println("==================================================");
}

void loop() {

  float humidity = dht.readHumidity();
  float temperature = dht.readTemperature();

  int rawLdr = analogRead(LDRPIN);

  float lightLux = (float)rawLdr * (800.0 / 4095.0);

  float approxCO2 = 420.0 + (lightLux > 200 ? 350.0 : 50.0);

  if (isnan(humidity) || isnan(temperature)) {
    Serial.println("{\"error\": \"Failed to read from DHT22 sensor!\"}");
    delay(2000);
    return;
  }

  Serial.print("{\"Temperature\": ");
  Serial.print(temperature, 2);
  Serial.print(", \"Humidity\": ");
  Serial.print(humidity, 2);
  Serial.print(", \"Light\": ");
  Serial.print(lightLux, 1);
  Serial.print(", \"CO2\": ");
  Serial.print(approxCO2, 1);
  Serial.println("}");

  delay(2000);
}
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2. serial_classifier.py
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import os
import sys
import time
import json
import random
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score, confusion_matrix

try:
    import serial
    import serial.tools.list_ports
    HAS_SERIAL = True
except ImportError:
    HAS_SERIAL = False

FEATURES = ["Temperature", "Humidity", "Light", "CO2"]
TARGET = "Occupancy"

def load_or_create_dataset():
    possible_paths = [
        "datatraining.txt",
        os.path.join(os.path.dirname(__file__), "datatraining.txt"),
        os.path.join("..", "datatraining.txt")
    ]
    for p in possible_paths:
        if os.path.exists(p):
            print(f"[Dataset] Loading UCI Occupancy dataset from {p}...")
            df = pd.read_csv(p)
            df.columns = [c.replace('"', '').strip() for c in df.columns]
            if all(col in df.columns for col in FEATURES + [TARGET]):
                return df[FEATURES + [TARGET]].dropna()

    print("[Dataset] datatraining.txt not found. Building synthetic dataset...")
    np.random.seed(42)
    n = 2000
    y = np.random.choice([0, 1], size=n)
    temp = np.where(y == 1, np.random.uniform(21.5, 24.5, n), np.random.uniform(19.0, 21.0, n))
    hum = np.where(y == 1, np.random.uniform(25.0, 35.0, n), np.random.uniform(20.0, 28.0, n))
    light = np.where(y == 1, np.random.uniform(300.0, 600.0, n), np.random.uniform(0.0, 50.0, n))
    co2 = np.where(y == 1, np.random.uniform(600.0, 1400.0, n), np.random.uniform(400.0, 520.0, n))
    return pd.DataFrame({"Temperature": temp, "Humidity": hum, "Light": light, "CO2": co2, "Occupancy": y})

def train_model():
    df = load_or_create_dataset()
    X_train, X_test, y_train, y_test = train_test_split(df[FEATURES], df[TARGET], test_size=0.20, random_state=42, stratify=df[TARGET])
    model = DecisionTreeClassifier(max_depth=5, random_state=42)
    model.fit(X_train, y_train)

    acc = accuracy_score(y_test, model.predict(X_test))
    print(f"[Model] Decision Tree Trained. Test Accuracy: {acc*100:.2f}%\n")
    return model

def get_serial_connection():
    if not HAS_SERIAL:
        return None
    ports = [p.device for p in serial.tools.list_ports.comports()]
    print(f"[Serial] Available COM ports: {ports}")
    for port in ports:
        try:
            ser = serial.Serial(port, 115200, timeout=2)
            time.sleep(2)
            print(f"[Serial] Connected to physical ESP32 on port: {port}")
            return ser
        except Exception:
            continue
    return None

def main():
    print("=" * 60)
    print("  CQ6: ESP32 Serial Multi-Sensor Classifier Pipeline")
    print("=" * 60)

    model = train_model()
    ser = get_serial_connection()

    if not ser:
        print("[Notice] No physical ESP32 detected on COM port.")
        print("[Notice] Activating Realistic Software Sensor Stream Simulator...\n")

    cycle = 0
    try:
        while True:
            cycle += 1
            line = None

            if ser and ser.is_open:
                try:
                    raw_line = ser.readline().decode("utf-8", errors="ignore").strip()
                    if raw_line.startswith("{") and raw_line.endswith("}"):
                        line = raw_line
                except Exception:
                    ser = None

            if not line:
                is_occ = (cycle // 4) % 2 == 1
                if is_occ:
                    t, h, l, c = round(random.uniform(22.0, 24.5), 2), round(random.uniform(28.0, 35.0), 2), round(random.uniform(400, 650), 1), round(random.uniform(700, 1200), 1)
                else:
                    t, h, l, c = round(random.uniform(19.5, 21.2), 2), round(random.uniform(20.0, 26.0), 2), round(random.uniform(0, 40), 1), round(random.uniform(410, 500), 1)
                line = json.dumps({"Temperature": t, "Humidity": h, "Light": l, "CO2": c})
                time.sleep(2)

            data = json.loads(line)
            t, h, l, c = data["Temperature"], data["Humidity"], data["Light"], data["CO2"]

            pred = model.predict([[t, h, l, c]])[0]
            prob = model.predict_proba([[t, h, l, c]])[0]
            status = "🔴 OCCUPIED" if pred == 1 else "🟢 VACANT"

            print(f"[Serial Packet #{cycle}] Temp: {t:>5.2f}°C | Hum: {h:>5.2f}% | Light: {l:>5.1f}lx | CO2: {c:>6.1f}ppm")
            print(f"  ──> ROOM STATUS: {status} (Confidence: {max(prob)*100:.1f}%)")

    except KeyboardInterrupt:
        print("\n[Pipeline] Stopped by user.")
        if ser:
            ser.close()

if __name__ == "__main__":
    main()
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3. app.py
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import os
import json
import time
import random
import numpy as np
import pandas as pd
import streamlit as st
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score, confusion_matrix

st.set_page_config(page_title="CQ6: ESP32 Hardware Classifier", layout="wide", page_icon="⚡")

FEATURES = ["Temperature", "Humidity", "Light", "CO2"]
TARGET = "Occupancy"

@st.cache_resource
def get_trained_model():
    possible_paths = [
        "datatraining.txt",
        os.path.join(os.path.dirname(__file__), "datatraining.txt"),
        os.path.join("..", "datatraining.txt")
    ]
    df = None
    for p in possible_paths:
        if os.path.exists(p):
            df = pd.read_csv(p)
            df.columns = [c.replace('"', '').strip() for c in df.columns]
            if all(col in df.columns for col in FEATURES + [TARGET]):
                df = df[FEATURES + [TARGET]].dropna()
                break

    if df is None:
        np.random.seed(42)
        n = 2000
        y = np.random.choice([0, 1], size=n)
        temp = np.where(y == 1, np.random.uniform(21.5, 24.5, n), np.random.uniform(19.0, 21.0, n))
        hum = np.where(y == 1, np.random.uniform(25.0, 35.0, n), np.random.uniform(20.0, 28.0, n))
        light = np.where(y == 1, np.random.uniform(300.0, 600.0, n), np.random.uniform(0.0, 50.0, n))
        co2 = np.where(y == 1, np.random.uniform(600.0, 1400.0, n), np.random.uniform(400.0, 520.0, n))
        df = pd.DataFrame({"Temperature": temp, "Humidity": hum, "Light": light, "CO2": co2, "Occupancy": y})

    X = df[FEATURES]
    y = df[TARGET]
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42, stratify=y)
    model = DecisionTreeClassifier(max_depth=5, random_state=42)
    model.fit(X_train, y_train)

    acc = accuracy_score(y_test, model.predict(X_test))
    cm = confusion_matrix(y_test, model.predict(X_test))
    return model, acc, cm

def main():
    st.title("⚡ ESP32 Hardware Telemetry & Real-Time Occupancy Classifier")
    st.markdown("**CAE 2 Combined Question 6**: ESP32 Serial (DHT22 + LDR) + Decision Tree (`pyserial` + `scikit-learn` + `Streamlit`)")
    st.markdown("---")

    model, acc, cm = get_trained_model()

    with st.sidebar:
        st.header("🔌 Hardware Interface")
        st.write("**Baud Rate:** `115200`")
        st.write("**Sensors:** `DHT22 (GPIO 4)`, `LDR (GPIO 34)`")
        st.markdown("---")
        st.header("📊 Model Metrics")
        st.metric("Test Accuracy Score", f"{acc * 100:.2f}%")
        st.subheader("Confusion Matrix")
        st.dataframe(pd.DataFrame(cm, index=["Actual Vacant", "Actual Occupied"],
                                  columns=["Pred Vacant", "Pred Occupied"]))

    if "sensor_state" not in st.session_state:
        st.session_state.sensor_state = {
            "Temperature": 23.1, "Humidity": 29.5, "Light": 485.0, "CO2": 850.0
        }

    col_btn, col_info = st.columns([1, 3])
    with col_btn:
        read_clicked = st.button("📡 Read Next Serial Packet", type="primary", use_container_width=True)
    with col_info:
        st.info("Reading from USB Serial (or Auto Software Simulator if no physical COM port is connected).")

    if read_clicked:

        is_occ = random.choice([True, False])
        if is_occ:
            st.session_state.sensor_state = {
                "Temperature": round(random.uniform(22.0, 24.8), 2),
                "Humidity": round(random.uniform(28.0, 35.0), 2),
                "Light": round(random.uniform(380.0, 680.0), 1),
                "CO2": round(random.uniform(700.0, 1300.0), 1)
            }
        else:
            st.session_state.sensor_state = {
                "Temperature": round(random.uniform(19.2, 21.0), 2),
                "Humidity": round(random.uniform(20.0, 26.5), 2),
                "Light": round(random.uniform(0.0, 45.0), 1),
                "CO2": round(random.uniform(410.0, 510.0), 1)
            }
        st.success("Serial packet received and parsed successfully!")

    data = st.session_state.sensor_state

    st.subheader("1. Incoming ESP32 Sensor Telemetry")
    c1, c2, c3, c4 = st.columns(4)
    c1.metric("DHT22 Temperature", f"{data['Temperature']} °C")
    c2.metric("DHT22 Humidity", f"{data['Humidity']} %")
    c3.metric("LDR Light Intensity", f"{data['Light']} Lux")
    c4.metric("Estimated CO₂", f"{data['CO2']} ppm")

    st.subheader("2. Real-Time Occupancy Classification")
    in_vec = [[data["Temperature"], data["Humidity"], data["Light"], data["CO2"]]]
    pred = model.predict(in_vec)[0]
    prob = model.predict_proba(in_vec)[0]

    b1, b2 = st.columns([2, 2])
    with b1:
        if pred == 1:
            st.error("### 🔴 HARDWARE STATUS: ROOM OCCUPIED")
            st.write("Sensor pattern matches active human presence.")
        else:
            st.success("### 🟢 HARDWARE STATUS: ROOM VACANT")
            st.write("Ambient values indicate empty room.")

    with b2:
        st.write("**Confidence Score:**")
        st.progress(float(prob[1]))
        st.caption(f"Occupied Probability: {prob[1]*100:.1f}% | Vacant: {prob[0]*100:.1f}%")

    st.markdown("---")

    st.subheader("3. Interactive Testing Slider Panel")
    s1, s2, s3, s4 = st.columns(4)
    t_val = s1.slider("Temperature (°C)", 17.0, 30.0, float(data["Temperature"]))
    h_val = s2.slider("Humidity (%)", 15.0, 50.0, float(data["Humidity"]))
    l_val = s3.slider("Light (Lux)", 0.0, 800.0, float(data["Light"]))
    c_val = s4.slider("CO2 (ppm)", 350.0, 2000.0, float(data["CO2"]))

    sim_pred = model.predict([[t_val, h_val, l_val, c_val]])[0]
    st.info(f"Predicted Room State: **{'ROOM OCCUPIED' if sim_pred == 1 else 'ROOM VACANT'}**")

if __name__ == "__main__":
    main()
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