Update app.py

app.py

@@ -1,13 +1,58 @@
 import gradio as gr
 import numpy as np
+import random
 
 # =========================
-# Prediction Function
+# DATABASE (temporary)
 # =========================
-def predict(duration, src_bytes, dst_bytes, failed_logins,
+users = {}
+
+# Store OTP temporarily
+otp_store = {}
+
+# =========================
+# SIGNUP
+# =========================
+def signup(username, password):
+    if username in users:
+        return "❌ User already exists"
+    
+    users[username] = password
+    return "✅ Signup successful! Please login."
+
+
+# =========================
+# LOGIN (STEP 1)
+# =========================
+def login(username, password):
+    if username in users and users[username] == password:
+        otp = random.randint(1000, 9999)
+        otp_store[username] = otp
+        return f"✅ OTP generated: {otp} (demo purpose)"
+    else:
+        return "❌ Invalid credentials"
+
+
+# =========================
+# OTP VERIFY (STEP 2)
+# =========================
+def verify_otp(username, otp_input):
+    if username in otp_store and str(otp_store[username]) == str(otp_input):
+        return "✅ Login Successful", True
+    else:
+        return "❌ Invalid OTP", False
+
+
+# =========================
+# PREDICT FUNCTION
+# =========================
+def predict(logged_in, duration, src_bytes, dst_bytes, failed_logins,
             count, error_rate, same_srv_rate, diff_srv_rate,
             host_count, login_attempts):
-    
+
+    if not logged_in:
+        return "🚫 Please login first"
+
     data = np.array([
         duration, src_bytes, dst_bytes, failed_logins,
         count, error_rate, same_srv_rate, diff_srv_rate,
@@ -16,78 +61,87 @@ def predict(duration, src_bytes, dst_bytes, failed_logins,
     
     score = np.sum(data)
 
-    # Define ranges
     if score < 30:
         status = "✅ Normal Traffic"
-        explanation = "Low activity and safe behavior detected."
-        risk_level = "Low"
+        explanation = "Low activity detected."
     elif score < 60:
         status = "⚠️ Suspicious Activity"
-        explanation = "Moderate anomaly detected. Could indicate unusual behavior."
-        risk_level = "Medium"
+        explanation = "Moderate anomaly detected."
     else:
         status = "🚨 Attack Detected!"
-        explanation = "High anomaly detected. Strong indication of malicious activity."
-        risk_level = "High"
+        explanation = "High anomaly detected."
 
     return f"""
 {status}
 
-### 🔢 Risk Score: {score:.2f}
-The risk score represents the overall intensity of network activity based on input features.
-
-### 📊 Interpretation:
-- **0 – 30 → Normal Traffic (Low Risk)**
-- **30 – 60 → Suspicious Activity (Medium Risk)**
-- **60+ → Attack (High Risk)**
+Risk Score: {score:.2f}
 
-### 🧠 Analysis:
-{explanation}
+0–30 → Normal  
+30–60 → Suspicious  
+60+ → Attack  
 
-### 🛡️ Recommendation:
-{"No action needed." if score < 30 else "Monitor the system." if score < 60 else "Immediate action required! Block or investigate."}
+Analysis: {explanation}
 """
 
 
 # =========================
-# UI Design
+# UI
 # =========================
 with gr.Blocks() as demo:
 
-    gr.Markdown("# 🔐 Intrusion Detection System (IDS)")
-    gr.Markdown(
-        "This system analyzes network traffic features and predicts whether the activity is **Normal** or a potential **Cyber Attack**.\n\n"
-        "👉 Enter values below to simulate network behavior."
-    )
+    gr.Markdown("# 🔐 Secure IDS with Authentication & 2FA")
+
+    # SIGNUP
+    gr.Markdown("## 📝 Signup")
+    su_user = gr.Textbox(label="Username")
+    su_pass = gr.Textbox(label="Password", type="password")
+    su_btn = gr.Button("Signup")
+    su_out = gr.Textbox()
+
+    su_btn.click(signup, inputs=[su_user, su_pass], outputs=su_out)
+
+    gr.Markdown("---")
 
-    with gr.Row():
-        duration = gr.Number(label="Connection Duration")
-        src_bytes = gr.Number(label="Source Bytes (Data Sent)")
-        dst_bytes = gr.Number(label="Destination Bytes (Data Received)")
+    # LOGIN
+    gr.Markdown("## 🔑 Login")
 
-    with gr.Row():
-        failed_logins = gr.Number(label="Failed Login Attempts")
-        login_attempts = gr.Number(label="Total Login Attempts")
-        count = gr.Number(label="Number of Connections")
+    li_user = gr.Textbox(label="Username")
+    li_pass = gr.Textbox(label="Password", type="password")
+    login_btn = gr.Button("Login")
 
-    with gr.Row():
-        error_rate = gr.Number(label="Error Rate")
-        same_srv_rate = gr.Number(label="Same Service Rate")
-        diff_srv_rate = gr.Number(label="Different Service Rate")
+    login_out = gr.Textbox()
 
-    host_count = gr.Number(label="Host Count")
+    login_btn.click(login, inputs=[li_user, li_pass], outputs=login_out)
 
-    predict_btn = gr.Button("🔍 Analyze Traffic")
+    # OTP
+    gr.Markdown("## 🔢 Enter OTP")
+
+    otp_input = gr.Textbox(label="OTP")
+    otp_btn = gr.Button("Verify OTP")
+
+    otp_out = gr.Textbox()
+    logged_state = gr.State(False)
+
+    otp_btn.click(
+        verify_otp,
+        inputs=[li_user, otp_input],
+        outputs=[otp_out, logged_state]
+    )
+
+    gr.Markdown("---")
+
+    # IDS
+    gr.Markdown("## 📊 Network Analysis")
+
+    inputs = [gr.Number(label=f"Feature {i+1}") for i in range(10)]
+
+    predict_btn = gr.Button("Analyze")
 
     output = gr.Markdown()
 
     predict_btn.click(
         predict,
-        inputs=[
-            duration, src_bytes, dst_bytes, failed_logins,
-            count, error_rate, same_srv_rate, diff_srv_rate,
-            host_count, login_attempts
-        ],
+        inputs=[logged_state] + inputs,
         outputs=output
     )