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app.py

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+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.")

columns.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:feac7566c51c13889ccc7a38deba65f0a810259427f1bd3928c6136998eec502
+size 148

latest_checkpoint.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6778348621ae9b28134b28017e24caf62feaeb9dedab5f9044685ec1a781543c
+size 1024456

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e684224bb39e6477c255482b7deccab315dd261c1f94f25c7bcc64b72bd6e96d
+size 538

shap_metadata.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c95ab9c7b97d14b502eeefaf2642f3bd6f4d30fd58cbdadf1be23873e1e689f2
+size 14731