Update app/streamlit_app.py

app/streamlit_app.py

@@ -17,17 +17,13 @@ from src.pipeline.predict_pipeline import PredictPipeline
 from src.explanability.shap_explainer import ShapExplainer
 from src.monitoring.db import save_to_db
 
-# ✅ Stable page config
-st.set_page_config(page_title="Fraud Guard", layout="wide", initial_sidebar_state="expanded")
+st.set_page_config(page_title="Fraud Guard", layout="wide")
 
 # --- Initialize Session State for Auto-Fill Buttons ---
-if "initialized" not in st.session_state:
-    st.session_state.t_time = 10000.0
-    st.session_state.t_amount = 100.0
-    for i in range(1, 29):
-        st.session_state[f"v_{i}"] = 0.0
-    st.session_state.initialized = True
-
+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"""
@@ -36,53 +32,51 @@ def generate_sample(is_fraud=False):
             {
                 "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]
+                       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]
+                       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,
+                       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]
             }
         ]
-
+        
         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]
-
+            st.session_state[f"v_{i}"] = chosen_fraud["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"])
 
@@ -98,7 +92,6 @@ if page == "Prediction":
 
     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):
@@ -110,281 +103,62 @@ if page == "Prediction":
         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):
-                    v_data = {}
                     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)
 
     with col2:
         st.markdown("### 📊 Live Telemetry & Assessment")
-
-        result_container = st.container()
-
         if not submit_btn:
             st.info("Awaiting transaction payload. Click 'Simulate' then 'Run Analysis'.")
 
         if submit_btn:
-            with result_container:
-                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}")
-                    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.progress(float(prob))
-                    st.markdown("---")
-
-                    st.subheader("🧠 Explainable AI (SHAP)")
-
-                    # ✅ NO CACHE HERE (fixes your error)
-                    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}")
-
-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!")
-
-    st.markdown("---")
-
-    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.stop()
-
-    if st.button("🚀 Run Drift Detection"):
-        with st.spinner("Running statistical drift analysis..."):
+            payload = {"Time": st.session_state.t_time, "Amount": st.session_state.t_amount, **v_data}
+            
             try:
-                report_path = detect_drift("data/creditcard.csv")
-                if report_path:
-                    st.success("✅ Drift report generated!")
-                    st.session_state["drift_done"] = True
+                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}")
                 else:
-                    st.warning("⚠️ Not enough data in live DB.")
-            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("---")
-
-    if st.session_state.get("drift_done", False):
-        if st.button("⚡ Retrain Model"):
-            with st.spinner("Retraining model..."):
-                try:
-                    retrain()
-                    st.success("✅ Model retrained successfully!")
-                except Exception as e:
-                    st.error(f"❌ Error: {e}")import streamlit as st
-import pandas as pd
-import sys
-import os
-import requests
-import shap
-import matplotlib.pyplot as plt
-import streamlit.components.v1 as components
-import random
-from dotenv import load_dotenv
-
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
-load_dotenv()
-API_URL = os.getenv("API_URL", "http://127.0.0.1:8000/predict")
-
-from src.pipeline.predict_pipeline import PredictPipeline
-from src.explanability.shap_explainer import ShapExplainer
-from src.monitoring.db import save_to_db
-
-# ✅ Stable page config
-st.set_page_config(page_title="Fraud Guard", layout="wide", initial_sidebar_state="expanded")
-
-# --- Initialize Session State for Auto-Fill Buttons ---
-if "initialized" not in st.session_state:
-    st.session_state.t_time = 10000.0
-    st.session_state.t_amount = 100.0
-    for i in range(1, 29):
-        st.session_state[f"v_{i}"] = 0.0
-    st.session_state.initialized = True
-
-
-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]
-            }
-        ]
-
-        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]
-
-    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"])
-
-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])
-
-    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")
-
-                with st.expander("PCA Feature Vectors (V1 - V28)", expanded=False):
-                    v_data = {}
-                    for i in range(1, 29):
-                        v_data[f"V{i}"] = st.number_input(f"V{i}", key=f"v_{i}", format="%.4f")
-
+                    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.progress(float(prob))
                 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)
-
-    with col2:
-        st.markdown("### 📊 Live Telemetry & Assessment")
-
-        result_container = st.container()
-
-        if not submit_btn:
-            st.info("Awaiting transaction payload. Click 'Simulate' then 'Run Analysis'.")
-
-        if submit_btn:
-            with result_container:
-                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}")
-                    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.subheader("🧠 Explainable AI (SHAP)")
+                
+                # ✅ FIX: No caching here (prevents shap hashing error)
+                fig, ax = plt.subplots(figsize=(8, 4))
+                shap.plots.waterfall(shap_values[0], show=False)
+                st.pyplot(fig)
 
-                    st.progress(float(prob))
-                    st.markdown("---")
-
-                    st.subheader("🧠 Explainable AI (SHAP)")
-
-                    # ✅ NO CACHE HERE (fixes your error)
-                    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}")
+            except Exception as e:
+                st.error(f"⏳ Error connecting to API: {e}")
 
 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"):
@@ -394,7 +168,7 @@ elif page == "Drift Monitoring":
                     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!")
+                st.success("✅ 50 Skewed rows injected! Now click 'Run Drift Detection' below.")
 
     st.markdown("---")
 
@@ -413,7 +187,7 @@ elif page == "Drift Monitoring":
                     st.success("✅ Drift report generated!")
                     st.session_state["drift_done"] = True
                 else:
-                    st.warning("⚠️ Not enough data in live DB.")
+                    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}")
 
@@ -424,12 +198,13 @@ elif page == "Drift Monitoring":
         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"):
+        if st.button("⚡ Retrain Model (Requires Verified Data)"):
             with st.spinner("Retraining model..."):
                 try:
                     retrain()
-                    st.success("✅ Model retrained successfully!")
+                    st.success("✅ Model retrained successfully with verified data!")
                 except Exception as e:
                     st.error(f"❌ Error: {e}")