Update app.py

app.py

@@ -1,3 +1,181 @@
 import trackio
+import gradio as gr
+import pandas as pd
+import numpy as np
+import plotly.graph_objects as go
+from sklearn.neural_network import MLPClassifier
+from sklearn.preprocessing import StandardScaler
+import hashlib
+import time
 
+# ===============================
+# CORPORATE LOGIN SYSTEM
+# ===============================
+
+USERNAME = "admin"
+PASSWORD = "manufacturing123"
+
+def authenticate(user, pwd):
+    if user == USERNAME and pwd == PASSWORD:
+        return gr.update(visible=True), gr.update(visible=False)
+    return gr.update(visible=False), gr.update(visible=True)
+
+# ===============================
+# LIVE IOT STREAM (PLC Simulation)
+# ===============================
+
+def generate_iot_data():
+    return pd.DataFrame({
+        "temp": np.random.normal(90, 15, 5),
+        "vibration": np.random.normal(0.6, 0.3, 5),
+        "output": np.random.normal(100, 20, 5)
+    })
+
+# ===============================
+# NEURAL NETWORK MODEL
+# ===============================
+
+base_data = generate_iot_data()
+base_data["failure"] = np.where(base_data["temp"] > 110, 1, 0)
+
+scaler = StandardScaler()
+X = scaler.fit_transform(base_data[["temp","vibration","output"]])
+y = base_data["failure"]
+
+model = MLPClassifier(hidden_layer_sizes=(32,16), max_iter=500)
+model.fit(X, y)
+
+# Save model hash for integrity
+def get_model_hash():
+    return hashlib.md5(str(model.coefs_).encode()).hexdigest()
+
+original_hash = get_model_hash()
+
+# ===============================
+# FGSM-STYLE ATTACK
+# ===============================
+
+def fgsm_attack(data, epsilon=5):
+    attacked = data.copy()
+    attacked["temp"] += epsilon
+    return attacked
+
+# ===============================
+# ATTACK ENGINE
+# ===============================
+
+def apply_attack(data, attack):
+
+    if attack == "Data Poisoning":
+        data["temp"] -= 40
+
+    elif attack == "Model Evasion":
+        data["temp"] = np.where(data["temp"] > 100, 99.5, data["temp"])
+
+    elif attack == "FGSM Adversarial":
+        data = fgsm_attack(data)
+
+    elif attack == "Model Replacement":
+        global model
+        model = MLPClassifier()  # replace with empty weak model
+
+    return data
+
+# ===============================
+# DEFENSE LAYER
+# ===============================
+
+def defense_layer(data):
+
+    if data["temp"].mean() < 50:
+        return "⚠ Data Poisoning Detected"
+
+    if get_model_hash() != original_hash:
+        return "⚠ Model Integrity Compromised"
+
+    if data["temp"].std() < 3:
+        return "⚠ Adversarial Pattern Detected"
+
+    return "✅ System Secure"
+
+# ===============================
+# MAIN SIMULATION
+# ===============================
+
+def run_system(attack, defense_toggle):
+
+    data = generate_iot_data()
+
+    before_scaled = scaler.transform(data[["temp","vibration","output"]])
+    before_pred = model.predict(before_scaled)
+
+    if attack != "None":
+        data = apply_attack(data, attack)
+
+    after_scaled = scaler.transform(data[["temp","vibration","output"]])
+    after_pred = model.predict(after_scaled)
+
+    risk_score = int(sum(after_pred) * 15)
+
+    if attack != "None" and defense_toggle == "OFF":
+        risk_score += 40
+
+    if defense_toggle == "ON":
+        defense_status = defense_layer(data)
+    else:
+        defense_status = "❌ Defense Disabled"
+
+    # Risk Gauge
+    gauge = go.Figure(go.Indicator(
+        mode="gauge+number",
+        value=risk_score,
+        title={'text': "Enterprise Risk Index"},
+        gauge={
+            'axis': {'range': [0,100]},
+            'steps': [
+                {'range':[0,30],'color':"green"},
+                {'range':[30,70],'color':"yellow"},
+                {'range':[70,100],'color':"red"}
+            ]
+        }
+    ))
+
+    # SCADA Live Chart
+    scada_chart = go.Figure()
+    scada_chart.add_trace(go.Bar(y=data["temp"], name="PLC Temperature"))
+    scada_chart.update_layout(title="Live SCADA - PLC Temperature")
+
+    return gauge, scada_chart, defense_status
+
+# ===============================
+# CORPORATE EXECUTIVE UI
+# ===============================
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+
+    gr.Markdown("# 🏭 Smart Factory AI Security Command Center")
+    gr.Markdown("Industry 4.0 | AI Attack Simulation | Executive Monitoring")
+
+    login_box = gr.Column()
+    dashboard_box = gr.Column(visible=False)
+
+    with login_box:
+        user = gr.Textbox(label="Username")
+        pwd = gr.Textbox(label="Password", type="password")
+        login_btn = gr.Button("Login")
+
+    with dashboard_box:
+        attack = gr.Dropdown(["None","Data Poisoning","Model Evasion","FGSM Adversarial","Model Replacement"], label="Select AI Attack")
+        defense = gr.Radio(["ON","OFF"], label="Defense System")
+
+        run_btn = gr.Button("Run Simulation")
+
+        risk_output = gr.Plot(label="Risk Gauge")
+        scada_output = gr.Plot(label="SCADA Live View")
+        defense_text = gr.Textbox(label="Defense Status")
+
+    login_btn.click(authenticate, [user, pwd], [dashboard_box, login_box])
+    run_btn.click(run_system, [attack, defense], [risk_output, scada_output, defense_text])
+
+demo.launch()
 trackio.show()