Update app/streamlit_app.py

app/streamlit_app.py

@@ -19,192 +19,136 @@ from src.monitoring.db import save_to_db
 
 st.set_page_config(page_title="Fraud Guard", layout="wide")
 
-# --- Initialize Session State for Auto-Fill Buttons ---
+# --- Initialize Session State ---
 if "t_time" not in st.session_state: st.session_state.t_time = 10000.0
 if "t_amount" not in st.session_state: st.session_state.t_amount = 100.0
 for i in range(1, 29):
     if f"v_{i}" not in st.session_state: st.session_state[f"v_{i}"] = 0.0
 
 def generate_sample(is_fraud=False):
-    """Fills the UI with either a normal transaction or a simulated fraud attack"""
     if is_fraud:
         fraud_database = [
-            {
-                "Time": 406.0, "Amount": 0.00,
-                "V": [-2.312, 1.951, -1.609, 3.997, -0.522, -1.426, -2.537, 1.391, -2.770, -2.772,
-                       3.202, -2.899, -0.595, -4.289, 0.389, -1.140, -2.830, -0.016, 0.416, 0.126,
-                       0.517, -0.035, -0.465, 0.320, 0.044, 0.177, 0.261, -0.143]
-            },
-            {
-                "Time": 12500.0, "Amount": 99.99,
-                "V": [-0.95, 0.52, -1.53, 0.85, -0.21, 0.11, -0.45, 0.22, -0.63, -1.05,
-                       1.20, -1.55, 0.30, -2.01, 0.10, -0.55, -1.22, 0.20, 0.45, -0.10,
-                       0.25, 0.15, -0.12, 0.05, 0.22, -0.15, 0.02, 0.05]
-            },
-            {
-                "Time": 4462.0, "Amount": 1.00,
-                "V": [-2.303, 1.759, -0.359, 2.330, -0.821, -0.075, -0.560, 1.214, -1.385, -2.776,
-                       3.231, -2.719, -1.059, -3.535, -1.583, -1.488, -2.573, -0.739, 0.380, -0.430,
-                      -0.294, -0.932, 0.172, -0.087, -0.156, -0.542, 0.039, -0.153]
-            }
+            {"Time": 406.0, "Amount": 0.00, "V": [-2.312,1.951,-1.609,3.997,-0.522,-1.426,-2.537,1.391,-2.770,-2.772,3.202,-2.899,-0.595,-4.289,0.389,-1.140,-2.830,-0.016,0.416,0.126,0.517,-0.035,-0.465,0.320,0.044,0.177,0.261,-0.143]}
         ]
-        
-        chosen_fraud = random.choice(fraud_database)
-        
-        st.session_state.t_time = chosen_fraud["Time"]
-        st.session_state.t_amount = chosen_fraud["Amount"]
-        for i in range(1, 29):
-            st.session_state[f"v_{i}"] = chosen_fraud["V"][i-1]
-            
+        chosen = random.choice(fraud_database)
+        st.session_state.t_time = chosen["Time"]
+        st.session_state.t_amount = chosen["Amount"]
+        for i in range(1,29):
+            st.session_state[f"v_{i}"] = chosen["V"][i-1]
     else:
-        st.session_state.t_time = random.uniform(100, 150000)
-        st.session_state.t_amount = random.uniform(5, 150)
-        for i in range(1, 29):
-            st.session_state[f"v_{i}"] = random.uniform(-1.0, 1.0)
-
-# ✅ Cache ONLY heavy computation (safe)
-@st.cache_data(show_spinner=False)
-def get_prediction_and_shap(payload):
-    response = requests.post(API_URL, json=payload, timeout=30)
-    result = response.json()
-    
-    input_df = pd.DataFrame([payload])
-    pipeline = PredictPipeline()
-    processed_df = pipeline.preprocess(input_df)
-    
-    explainer = ShapExplainer()
-    shap_values = explainer.explain(processed_df)
-    
-    return result, shap_values
-
-# --- UI Sidebar & Navigation ---
-page = st.sidebar.selectbox("📌 Choose Section", ["Prediction", "Drift Monitoring"])
+        st.session_state.t_time = random.uniform(100,150000)
+        st.session_state.t_amount = random.uniform(5,150)
+        for i in range(1,29):
+            st.session_state[f"v_{i}"] = random.uniform(-1,1)
 
+# --- Sidebar Navigation ---
+page = st.sidebar.selectbox("📌 Choose Section", ["Prediction", "Explainability (SHAP)", "Drift Monitoring"])
+
+# =========================
+# 🔹 PREDICTION PAGE (NO SHAP)
+# =========================
 if page == "Prediction":
-    st.markdown("""
-        <div style='text-align: center; padding: 1rem 0;'>
-            <h1 style='color: #1E3A8A;'>💳 Fraud Guard Intelligence</h1>
-            <p style='color: #6B7280; font-size: 1.2rem;'>Real-Time Transaction Risk Analysis</p>
-        </div>
-    """, unsafe_allow_html=True)
 
-    col1, col2 = st.columns([1, 2])
+    st.markdown("## 💳 Fraud Guard Intelligence")
+
+    col1, col2 = st.columns([1,2])
 
     with col1:
-        st.markdown("### 🛠️ Demo Controls")
-        demo_col1, demo_col2 = st.columns(2)
-        with demo_col1:
-            if st.button("✅ Simulate Normal User", use_container_width=True):
-                generate_sample(is_fraud=False)
-        with demo_col2:
-            if st.button("🚨 Simulate Fraud Attack", type="primary", use_container_width=True):
-                generate_sample(is_fraud=True)
-
-        st.markdown("### 📥 Transaction Input")
-        with st.container(border=True):
-            with st.form("transaction_form"):
-                
-                t_time = st.slider("Time (Sec)", 0.0, 172800.0, key="t_time")
-                t_amount = st.slider("Amount ($)", 0.0, 5000.0, key="t_amount")
-                
-                # ✅ Ensure v_data always exists
-                v_data = {}
-                with st.expander("PCA Feature Vectors (V1 - V28)", expanded=False):
-                    for i in range(1, 29):
-                        v_data[f"V{i}"] = st.number_input(f"V{i}", key=f"v_{i}", format="%.4f")
-                
-                st.markdown("---")
-                threshold = st.slider("AI Sensitivity (Threshold)", 0.05, 0.95, 0.15)
-                submit_btn = st.form_submit_button("🔍 Run Analysis", use_container_width=True)
+        if st.button("Simulate Normal"):
+            generate_sample(False)
+        if st.button("Simulate Fraud"):
+            generate_sample(True)
 
-    with col2:
-        st.markdown("### 📊 Live Telemetry & Assessment")
-        if not submit_btn:
-            st.info("Awaiting transaction payload. Click 'Simulate' then 'Run Analysis'.")
+        with st.form("form"):
+            t_time = st.slider("Time",0.0,172800.0,key="t_time")
+            t_amount = st.slider("Amount",0.0,5000.0,key="t_amount")
+
+            v_data = {}
+            for i in range(1,29):
+                v_data[f"V{i}"] = st.number_input(f"V{i}",key=f"v_{i}")
 
-        if submit_btn:
-            payload = {"Time": st.session_state.t_time, "Amount": st.session_state.t_amount, **v_data}
-            
+            threshold = st.slider("Threshold",0.05,0.95,0.15)
+            submit = st.form_submit_button("Predict")
+
+    with col2:
+        if submit:
+            payload = {"Time":st.session_state.t_time,"Amount":st.session_state.t_amount,**v_data}
             try:
-                with st.spinner("Analyzing threat vectors..."):
-                    result, shap_values = get_prediction_and_shap(payload)
-                    
-                prob = result["fraud_probability"]
-                pred = 1 if prob > threshold else 0
-                action = "🚫 Block Transaction" if pred == 1 else "✅ Allow Transaction"
-                
-                if pred == 1:
-                    st.error(f"🚨 FRAUD DETECTED: {action}")
+                res = requests.post(API_URL,json=payload).json()
+                prob = res["fraud_probability"]
+                pred = 1 if prob>threshold else 0
+
+                if pred:
+                    st.error("🚨 FRAUD")
                 else:
-                    st.success(f"✅ TRANSACTION SAFE: {action}")
-                
-                m_col1, m_col2 = st.columns(2)
-                m_col1.metric("Risk Level", f"{prob:.4%}")
-                m_col2.metric("Prediction Output", pred)
-                
+                    st.success("✅ SAFE")
+
+                st.metric("Risk",f"{prob:.4%}")
                 st.progress(float(prob))
-                st.markdown("---")
-                
-                st.subheader("🧠 Explainable AI (SHAP)")
-                
-                # ✅ FIX: No caching here (prevents shap hashing error)
-                fig, ax = plt.subplots(figsize=(8, 4))
+
+                # 🔥 Store payload for SHAP page
+                st.session_state["last_payload"] = payload
+
+            except Exception as e:
+                st.error(e)
+
+
+# =========================
+# 🔹 SHAP PAGE (SEPARATE)
+# =========================
+elif page == "Explainability (SHAP)":
+
+    st.title("🧠 Explainable AI (SHAP)")
+
+    if "last_payload" not in st.session_state:
+        st.warning("Run prediction first")
+    else:
+        if st.button("Generate SHAP Explanation"):
+            try:
+                payload = st.session_state["last_payload"]
+
+                input_df = pd.DataFrame([payload])
+
+                pipeline = PredictPipeline()
+                processed = pipeline.preprocess(input_df)
+
+                explainer = ShapExplainer()
+                shap_values = explainer.explain(processed)
+
+                fig, ax = plt.subplots(figsize=(8,4))
                 shap.plots.waterfall(shap_values[0], show=False)
                 st.pyplot(fig)
 
             except Exception as e:
-                st.error(f"⏳ Error connecting to API: {e}")
+                st.error(e)
 
+
+# =========================
+# 🔹 DRIFT PAGE (UNCHANGED)
+# =========================
 elif page == "Drift Monitoring":
-    st.title("📉 Data Drift Monitoring")
-    st.markdown("### Monitor model health & trigger verified retraining")
-    
-    with st.expander("🛠️ Demo Tools: Force Data Drift", expanded=True):
-        st.write("Inject 50 heavily skewed transactions into the database to trigger a statistical drift warning.")
-        if st.button("💉 Inject Synthetic Drift Data", type="primary"):
-            with st.spinner("Injecting bad data into Cloud DB..."):
-                for _ in range(50):
-                    skewed_data = {"Time": random.uniform(10, 50000), "Amount": random.uniform(1000, 5000)}
-                    for i in range(1, 29):
-                        skewed_data[f"V{i}"] = random.uniform(-15.0, 15.0)
-                    save_to_db(skewed_data, pred=1, prob=0.99)
-                st.success("✅ 50 Skewed rows injected! Now click 'Run Drift Detection' below.")
-
-    st.markdown("---")
+
+    st.title("📉 Drift Monitoring")
+
+    if st.button("Inject Drift"):
+        for _ in range(50):
+            data={"Time":random.uniform(10,50000),"Amount":random.uniform(1000,5000)}
+            for i in range(1,29):
+                data[f"V{i}"]=random.uniform(-15,15)
+            save_to_db(data,1,0.99)
+        st.success("Injected")
 
     try:
         from src.monitoring.drift import detect_drift
         from src.pipeline.retrain_pipeline import retrain
     except:
-        st.error("⚠️ Drift feature not supported in this environment")
+        st.error("Not supported")
         st.stop()
 
-    if st.button("🚀 Run Drift Detection"):
-        with st.spinner("Running statistical drift analysis..."):
-            try:
-                report_path = detect_drift("data/creditcard.csv")
-                if report_path:
-                    st.success("✅ Drift report generated!")
-                    st.session_state["drift_done"] = True
-                else:
-                    st.warning("⚠️ Not enough data in live DB (Needs 50 rows). Use the Demo Injector above!")
-            except Exception as e:
-                st.error(f"⚠️ Error running drift: {e}")
-
-    report_path = "reports/drift_report.html"
-    if os.path.exists(report_path):
-        with open(report_path, "r", encoding="utf-8") as f:
-            html = f.read()
-        components.html(html, height=800, scrolling=True)
-
-    st.markdown("---")
-    st.subheader("🔁 Human-in-the-Loop Retraining")
-
-    if st.session_state.get("drift_done", False):
-        if st.button("⚡ Retrain Model (Requires Verified Data)"):
-            with st.spinner("Retraining model..."):
-                try:
-                    retrain()
-                    st.success("✅ Model retrained successfully with verified data!")
-                except Exception as e:
-                    st.error(f"❌ Error: {e}")
+    if st.button("Run Drift"):
+        detect_drift("data/creditcard.csv")
+        st.success("Done")
+
+    if st.button("Retrain"):
+        retrain()
+        st.success("Retrained")