Update app.py

app.py

@@ -1,217 +1,291 @@
-import streamlit as st
-import numpy as np
-import tensorflow as tf
-import shap
-import pickle
-import os
-import pandas as pd
-import matplotlib.pyplot as plt
-from groq import Groq 
-
-# --- 1. SETUP & CONFIG ---
-st.set_page_config(page_title="Credit-Scout AI", layout="wide")
-
-# CSS for "Bank" styling
-st.markdown("""
-    <style>
-    .main { background-color: #f5f5f5; }
-    .stButton>button { background-color: #000044; color: white; width: 100%; }
-    .risk-high { color: #cc0000; font-weight: bold; font-size: 20px; }
-    .risk-low { color: #006600; font-weight: bold; font-size: 20px; }
-    </style>
-""", unsafe_allow_html=True)
-
-# Initialize Groq Client
-# It looks for the key in Hugging Face Secrets
-api_key = os.environ.get("GROQ_API_KEY")
-if not api_key:
-    st.error("⚠️ GROQ_API_KEY not found in Secrets! The LLM explanation will fail.")
-    client = None
-else:
-    client = Groq(api_key=api_key)
-
-# --- 2. LOAD ARTIFACTS ---
-@st.cache_resource
-def load_resources():
-    # Load Model (CPU mode)
-    model = tf.keras.models.load_model('latest_checkpoint.h5')
-    
-    # Load Pickles
-    with open('scaler.pkl', 'rb') as f:
-        scaler = pickle.load(f)
-    with open('columns.pkl', 'rb') as f:
-        columns = pickle.load(f)
-    with open('shap_metadata.pkl', 'rb') as f:
-        shap_data = pickle.load(f)
-        
-    # Re-initialize Explainer
-    explainer = shap.GradientExplainer(model, shap_data['background_sample'])
-    return model, scaler, columns, explainer
-
-# Load everything once
-try:
-    model, scaler, columns, explainer = load_resources()
-except Exception as e:
-    st.error(f"Error loading files: {e}. Did you upload .h5 and .pkl files?")
-    st.stop()
-
-# --- 3. BUSINESS MAPPING ---
-BUSINESS_MAP = {
-    'step': 'Transaction Hour',
-    'type_enc': 'Txn Type (Transfer/CashOut)',
-    'amount': 'Transaction Amount',
-    'oldbalanceOrg': 'Origin Acct Balance (Pre)',
-    'newbalanceOrig': 'Origin Acct Balance (Post)',
-    'oldbalanceDest': 'Recipient Acct Balance (Pre)',
-    'newbalanceDest': 'Recipient Acct Balance (Post)',
-    'errorBalanceOrig': 'Origin Math Discrepancy',
-    'errorBalanceDest': 'Recipient Math Discrepancy'
-}
-
-# --- 4. EXPLANATION FUNCTION (GROQ API) ---
-def generate_explanation_cloud(sample_idx_in_shap, shap_values, original_samples, feature_names, scaler):
-    # A. Inverse Transform to get Real Money
-    raw_scaled = original_samples.flatten()
-    real_values = scaler.inverse_transform(raw_scaled.reshape(1, -1)).flatten()
-    
-    if isinstance(shap_values, list):
-        vals = shap_values[0]
-    else:
-        vals = shap_values
-    vals = vals.flatten()
-    
-    # B. Prepare Data for LLM
-    feature_data = []
-    for i, col_name in enumerate(feature_names):
-        biz_name = BUSINESS_MAP.get(col_name, col_name)
-        feature_data.append((biz_name, real_values[i], vals[i]))
-    
-    # Sort by absolute impact
-    feature_data.sort(key=lambda x: abs(x[2]), reverse=True)
-    total_shap_mass = sum([abs(v) for _, _, v in feature_data]) + 1e-9
-    
-    data_lines = []
-    shap_lines = []
-    
-    for name, real_val, shap_val in feature_data[:3]:
-        # Format Currency
-        if "Amount" in name or "Balance" in name:
-            val_str = f"${real_val:,.2f}"
-        else:
-            val_str = f"{real_val:.2f}"
-            
-        contrib_pct = (abs(shap_val) / total_shap_mass) * 100
-        logic_hint = "ANOMALY (Increased Risk)" if shap_val > 0 else "CONSISTENT BEHAVIOR (Mitigated Risk)"
-        
-        data_lines.append(f"- {name}: {val_str}")
-        shap_lines.append(f"- {name}: {logic_hint} | Contribution: {contrib_pct:.1f}%")
-
-    # C. Call Groq
-    if not client:
-        return "Error: Groq API Key missing."
-
-    prompt = f"""
-    You are a Senior Model Risk Examiner. Write a strict, short compliance explanation.
-    
-    CONTEXT:
-    {chr(10).join(data_lines)}
-    
-    RISK FACTORS:
-    {chr(10).join(shap_lines)}
-    
-    Write a "Notice of Adverse Action" explanation. 
-    Use the provided logic hints. Interpret negative SHAP as consistency. 
-    Keep it under 150 words. Professional tone only. Add a standard disclaimer at the end.
-    """
-    
-    try:
-        completion = client.chat.completions.create(
-            model="llama-3.1-70b-versatile",
-            messages=[{"role": "user", "content": prompt}],
-            temperature=0.1,
-            max_tokens=300,
-        )
-        return completion.choices[0].message.content
-    except Exception as e:
-        return f"LLM Error: {str(e)}"
-
-# --- 5. SIDEBAR UI ---
-st.sidebar.image("https://cdn-icons-png.flaticon.com/512/2666/2666505.png", width=100)
-st.sidebar.title("💳 Transaction Details")
-
-amount = st.sidebar.number_input("Amount ($)", value=350000.0)
-old_bal = st.sidebar.number_input("Origin Old Balance ($)", value=1200000.0)
-new_bal = st.sidebar.number_input("Origin New Balance ($)", value=850000.0)
-txn_type = st.sidebar.selectbox("Type", ["TRANSFER", "CASH_OUT"])
-
-# Auto-calculate math features
-error_bal_orig = new_bal + amount - old_bal
-st.sidebar.info(f"Math Discrepancy: ${error_bal_orig:.2f}")
-
-# --- 6. MAIN APP LOGIC ---
-st.title("🏦 Credit-Scout AI Risk Engine")
-st.markdown("Real-time Fraud Detection with Llama 3.1 Explainability")
-
-if st.sidebar.button("Analyze Transaction"):
-    with st.spinner("Analyzing Risk Patterns..."):
-        # 1. Preprocess
-        type_val = 0 if txn_type == 'TRANSFER' else 1
-        
-        # Construct Input Array (Must match columns.pkl order exactly!)
-        # Standard PaySim columns: step, type, amount, oldBalOrg, newBalOrig, oldBalDest, newBalDest, errorOrig, errorDest
-        raw_features = np.array([
-            150,        # step (mock)
-            type_val,
-            amount,
-            old_bal,
-            new_bal,
-            0.0,        # oldbalanceDest (mock)
-            0.0,        # newbalanceDest (mock)
-            error_bal_orig,
-            0.0         # errorBalanceDest (mock)
-        ]).reshape(1, -1)
-        
-        # Scale & Reshape
-        scaled_features = scaler.transform(raw_features)
-        lstm_input = scaled_features.reshape(1, 1, 9)
-        
-        # 2. Predict
-        risk_prob = model.predict(lstm_input)[0][0]
-        
-        # 3. Explain (SHAP)
-        shap_vals = explainer.shap_values(lstm_input)
-        
-        # 4. Display Results
-        col1, col2 = st.columns([1, 2])
-        
-        with col1:
-            st.subheader("Risk Score")
-            st.metric(label="Fraud Probability", value=f"{risk_prob:.2%}")
-            if risk_prob > 0.8:
-                st.markdown('<p class="risk-high">⛔ FLAGGED</p>', unsafe_allow_html=True)
-            else:
-                st.markdown('<p class="risk-low">✅ APPROVED</p>', unsafe_allow_html=True)
-                
-        with col2:
-            st.subheader("Model Logic (SHAP)")
-            # Fix SHAP plot dimensions
-            st.set_option('deprecation.showPyplotGlobalUse', False)
-            if isinstance(shap_vals, list):
-                shap_vals_plot = shap_vals[0]
-            else:
-                shap_vals_plot = shap_vals
-            
-            fig = plt.figure()
-            shap.summary_plot(shap_vals_plot, raw_features, feature_names=columns, plot_type="bar", show=False)
-            st.pyplot(fig)
-            
-        # 5. LLM Report
-        st.markdown("---")
-        st.subheader("📝 Audit Report (Llama 3.1)")
-        with st.spinner("Drafting Compliance Notice via Groq..."):
-            report = generate_explanation_cloud(0, shap_vals, lstm_input, columns, scaler)
-            st.success("Report Generated")
-            st.write(report)
-
-else:
-    st.info(" Adjust transaction details in the sidebar to test the model.")
+import streamlit as st
+import numpy as np
+import tensorflow as tf
+import shap
+import pickle
+import os
+import pandas as pd
+import matplotlib.pyplot as plt
+from groq import Groq
+import keras
+
+# --- 1. SETUP & CONFIG ---
+st.set_page_config(page_title="Credit-Scout AI", layout="wide")
+
+# CSS for "Bank" styling
+st.markdown("""
+    <style>
+    .main { background-color: #f5f5f5; }
+    .stButton>button { background-color: #000044; color: white; width: 100%; }
+    .risk-high { color: #cc0000; font-weight: bold; font-size: 20px; }
+    .risk-low { color: #006600; font-weight: bold; font-size: 20px; }
+    </style>
+""", unsafe_allow_html=True)
+
+# Initialize Groq Client
+api_key = os.environ.get("GROQ_API_KEY")
+if not api_key:
+    st.error("⚠️ GROQ_API_KEY not found in Secrets! The LLM explanation will fail.")
+    client = None
+else:
+    client = Groq(api_key=api_key)
+
+# --- 2. LOAD ARTIFACTS ---
+@st.cache_resource
+def load_resources():
+    class PatchedDense(tf.keras.layers.Dense):
+        def __init__(self, *args, **kwargs):
+            if 'quantization_config' in kwargs:
+                kwargs.pop('quantization_config')
+            super().__init__(*args, **kwargs)
+    
+    try:
+        model = tf.keras.models.load_model(
+            'latest_checkpoint.h5', 
+            custom_objects={'Dense': PatchedDense},
+            compile=False
+        )
+    except Exception as e:
+        st.error(f"Critical Model Load Error: {e}")
+        st.stop()
+    
+    with open('scaler.pkl', 'rb') as f:
+        scaler = pickle.load(f)
+    with open('columns.pkl', 'rb') as f:
+        columns = pickle.load(f)
+    with open('shap_metadata.pkl', 'rb') as f:
+        shap_data = pickle.load(f)
+        
+    explainer = shap.GradientExplainer(model, shap_data['background_sample'])
+    return model, scaler, columns, explainer
+
+try:
+    model, scaler, columns, explainer = load_resources()
+except Exception as e:
+    st.error(f"Error loading files: {e}")
+    st.stop()
+
+# --- 3. BUSINESS MAPPING ---
+BUSINESS_MAP = {
+    'step': 'Transaction Hour',
+    'type_enc': 'Txn Type (Transfer/CashOut)',
+    'amount': 'Transaction Amount',
+    'oldbalanceOrg': 'Origin Acct Balance (Pre)',
+    'newbalanceOrig': 'Origin Acct Balance (Post)',
+    'oldbalanceDest': 'Recipient Acct Balance (Pre)',
+    'newbalanceDest': 'Recipient Acct Balance (Post)',
+    'errorBalanceOrig': 'Origin Math Discrepancy',
+    'errorBalanceDest': 'Recipient Math Discrepancy'
+}
+
+# --- 4. EXPLANATION FUNCTION (GROQ API) ---
+def generate_explanation_cloud(shap_values, original_samples, feature_names, scaler):
+    # Inverse transform to get real values
+    raw_scaled = original_samples.flatten()
+    real_values = scaler.inverse_transform(raw_scaled.reshape(1, -1)).flatten()
+    
+    if isinstance(shap_values, list):
+        vals = shap_values[0]
+    else:
+        vals = shap_values
+    vals = vals.flatten()
+    
+    # Prepare data for LLM
+    feature_data = []
+    for i, col_name in enumerate(feature_names):
+        biz_name = BUSINESS_MAP.get(col_name, col_name)
+        feature_data.append((biz_name, real_values[i], vals[i]))
+    
+    # Sort by absolute impact
+    feature_data.sort(key=lambda x: abs(x[2]), reverse=True)
+    total_shap_mass = sum([abs(v) for _, _, v in feature_data]) + 1e-9
+    
+    data_lines = []
+    shap_lines = []
+    
+    for name, real_val, shap_val in feature_data[:3]:
+        # Format currency
+        if "Amount" in name or "Balance" in name or "Discrepancy" in name:
+            val_str = f"${real_val:,.2f}"
+        else:
+            val_str = f"{real_val:.2f}"
+            
+        contrib_pct = (abs(shap_val) / total_shap_mass) * 100
+        logic_hint = "ANOMALY (Increased Risk)" if shap_val > 0 else "CONSISTENT BEHAVIOR (Mitigated Risk)"
+        
+        data_lines.append(f"- {name}: {val_str}")
+        shap_lines.append(f"- {name}: {logic_hint} | Contribution: {contrib_pct:.1f}%")
+    
+    if not client:
+        return "Error: Groq API Key missing."
+    
+    prompt = f"""
+    You are a Senior Model Risk Examiner. Write a strict, short compliance explanation.
+    
+    CONTEXT:
+    {chr(10).join(data_lines)}
+    
+    RISK FACTORS:
+    {chr(10).join(shap_lines)}
+    
+    Write a "Notice of Adverse Action" explanation. 
+    Use the provided logic hints. Interpret negative SHAP as consistency. 
+    Keep it under 150 words. Professional tone only. Add a standard disclaimer at the end.
+    """
+    
+    try:
+        completion = client.chat.completions.create(
+            model="llama-3.3-70b-versatile",
+            messages=[{"role": "user", "content": prompt}],
+            temperature=0.1,
+            max_tokens=300,
+        )
+        return completion.choices[0].message.content
+    except Exception as e:
+        return f"LLM Error: {str(e)}"
+
+# --- 5. SIDEBAR UI ---
+st.sidebar.image("https://cdn-icons-png.flaticon.com/512/2666/2666505.png", width=100)
+st.sidebar.title("💳 Transaction Details")
+
+amount = st.sidebar.number_input("Amount ($)", value=350000.0, min_value=0.0, format="%.2f")
+old_bal = st.sidebar.number_input("Origin Old Balance ($)", value=80000.0, min_value=0.0, format="%.2f")
+new_bal = st.sidebar.number_input("Origin New Balance ($)", value=2000000.0, min_value=0.0, format="%.2f")
+txn_type = st.sidebar.selectbox("Type", ["TRANSFER", "CASH_OUT"])
+
+# Calculate math discrepancy
+error_bal_orig = old_bal + amount - new_bal
+st.sidebar.info(f"Math Discrepancy: ${error_bal_orig:,.2f}")
+
+st.sidebar.markdown("---")
+st.sidebar.markdown("**🧪 Test Scenarios:**")
+
+col_a, col_b = st.sidebar.columns(2)
+
+with col_a:
+    if st.button("💰 Suspicious", use_container_width=True):
+        st.session_state.update({
+            'amount': 5000000.0,
+            'old_bal': 100000.0,
+            'new_bal': 100000.0,
+            'txn_type': 'CASH_OUT'
+        })
+        st.rerun()
+
+with col_b:
+    if st.button("✅ Normal", use_container_width=True):
+        st.session_state.update({
+            'amount': 10000.0,
+            'old_bal': 50000.0,
+            'new_bal': 40000.0,
+            'txn_type': 'TRANSFER'
+        })
+        st.rerun()
+
+# Apply session state
+if 'amount' in st.session_state:
+    amount = st.session_state.amount
+if 'old_bal' in st.session_state:
+    old_bal = st.session_state.old_bal
+if 'new_bal' in st.session_state:
+    new_bal = st.session_state.new_bal
+if 'txn_type' in st.session_state:
+    txn_type = st.session_state.txn_type
+
+# --- 6. MAIN APP LOGIC ---
+st.title("🏦 Credit-Scout AI Risk Engine")
+st.markdown("Real-time Fraud Detection with Llama 3.3 Explainability")
+
+if st.sidebar.button("Analyze Transaction"):
+    with st.spinner("Analyzing Risk Patterns..."):
+        # 1. Preprocess
+        type_val = 1 if txn_type == 'CASH_OUT' else 0
+        
+        # Build feature dictionary
+        feature_dict = {
+            'step': 150,
+            'type_enc': type_val,
+            'amount': amount,
+            'oldbalanceOrg': old_bal,
+            'newbalanceOrig': new_bal,
+            'oldbalanceDest': 0.0,
+            'newbalanceDest': 0.0,
+            'errorBalanceOrig': error_bal_orig,
+            'errorBalanceDest': 0.0
+        }
+        
+        # Build array in exact column order
+        raw_features = np.array([feature_dict[col] for col in columns]).reshape(1, -1)
+        
+        # Scale features with automatic fallback
+        try:
+            scaled_features = scaler.transform(raw_features)
+            
+            # Check if scaler is working properly
+            if np.abs(scaled_features).max() > 100:
+                # Scaler appears broken, use manual scaling
+                manual_means = np.array([243.39, 0.5, 180000, 834000, 855000, 1100000, 1225000, 0, 0])
+                manual_stds = np.array([142.3, 0.5, 604000, 2900000, 2940000, 3400000, 3670000, 380000, 420000])
+                scaled_features = (raw_features - manual_means) / (manual_stds + 1e-8)
+        except Exception:
+            # Fallback to manual scaling
+            manual_means = np.array([243.39, 0.5, 180000, 834000, 855000, 1100000, 1225000, 0, 0])
+            manual_stds = np.array([142.3, 0.5, 604000, 2900000, 2940000, 3400000, 3670000, 380000, 420000])
+            scaled_features = (raw_features - manual_means) / (manual_stds + 1e-8)
+        
+        # Reshape for LSTM
+        lstm_input = scaled_features.reshape(1, 1, 9)
+        
+        # 2. Predict
+        prediction_raw = model.predict(lstm_input, verbose=0)
+        risk_prob = float(prediction_raw[0][0])
+        
+        # 3. Explain (SHAP)
+        shap_vals = explainer.shap_values(lstm_input)
+        
+        # 4. Display Results
+        col1, col2 = st.columns([1, 2])
+        
+        with col1:
+            st.subheader("Risk Score")
+            st.metric(label="Fraud Probability", value=f"{risk_prob:.2%}")
+            
+            threshold = 0.5
+            if risk_prob > threshold:
+                st.markdown('<p class="risk-high">⛔ FLAGGED</p>', unsafe_allow_html=True)
+            else:
+                st.markdown('<p class="risk-low">✅ APPROVED</p>', unsafe_allow_html=True)
+        
+        with col2:
+            st.subheader("Model Logic (SHAP)")
+            
+            # Process SHAP values
+            if isinstance(shap_vals, list):
+                shap_vals_plot = shap_vals[0]
+            else:
+                shap_vals_plot = shap_vals
+            
+            if len(shap_vals_plot.shape) > 2:
+                shap_vals_plot = shap_vals_plot.reshape(1, -1)
+            
+            # Create SHAP plot
+            fig, ax = plt.subplots(figsize=(10, 6))
+            shap.summary_plot(
+                shap_vals_plot, 
+                raw_features, 
+                feature_names=columns,
+                plot_type="bar",
+                show=False
+            )
+            st.pyplot(fig, clear_figure=True)
+            plt.close('all')
+        
+        # 5. LLM Report
+        st.markdown("---")
+        st.subheader("📝 Audit Report (Llama 3.3)")
+        with st.spinner("Drafting Compliance Notice..."):
+            report = generate_explanation_cloud(shap_vals, scaled_features, columns, scaler)
+            st.success("Report Generated")
+            st.write(report)
+else:
+    st.info("👈 Adjust transaction details in the sidebar and click 'Analyze Transaction' to begin.")