Update src/streamlit_app.py

src/streamlit_app.py

@@ -5,7 +5,6 @@ import pandas as pd
 import numpy as np
 import joblib
 import json
-import uuid
 from datetime import datetime
 from huggingface_hub import hf_hub_download
 
@@ -134,7 +133,7 @@ def load_assets():
 model, encoders, scaler, config = load_assets()
 
 st.title("FinTech Fraud Guard")
-st.markdown("### MoE Transaction Verifier - Optimized Threshold: 0.9898")
+st.markdown("MoE Transaction Verifier - Optimized Threshold: 0.9898")
 
 with st.form("transaction_form"):
     c1, c2, c3 = st.columns(3)
@@ -143,7 +142,7 @@ with st.form("transaction_form"):
         st.subheader("Personal & Card")
         first = st.text_input("First Name", "Jeff")
         last = st.text_input("Last Name", "Elliott")
-        gender = st.selectbox("Gender", ["M", "F"], index=0)
+        gender = st.selectbox("Gender", ["M", "F"])
         dob = st.text_input("Date of Birth (DD-MM-YYYY)", "19-03-1968")
         cc_num = st.text_input("CC Number", "3725537864060026")
         job = st.text_input("Job", "Mechanical engineer")
@@ -167,7 +166,6 @@ with st.form("transaction_form"):
         lon = st.number_input("Customer Long", value=-80.9355)
         m_lat = st.number_input("Merchant Lat", value=33.986391)
         m_lon = st.number_input("Merchant Long", value=-81.200714)
-        trans_num = str(uuid.uuid4())[:16]
         
     submit = st.form_submit_button("ANALYZE TRANSACTION", use_container_width=True)
 
@@ -222,9 +220,9 @@ if submit:
             elif col == 'zip':
                 val = str(int(float(zip_v)))
             else:
-                val = str(cat_feats_order.index(col))
+                val = "unknown"
             
-            if hasattr(encoders[col], 'classes_') and val in encoders[col].classes_:
+            if col in encoders and hasattr(encoders[col], 'classes_') and val in encoders[col].classes_:
                 cat_encoded.append(encoders[col].transform([val])[0])
             else:
                 cat_encoded.append(0)
@@ -238,52 +236,41 @@ if submit:
             prob = torch.sigmoid(logits).item()
         
         st.divider()
-        col1, col2, col3 = st.columns([2, 1, 2])
         
-        with col2:
-            if prob > 0.9898:
-                st.markdown(f'''
-                <div class="fraud-card">
-                    <h2>FRAUD DETECTED</h2>
-                    <h3>Confidence: <span style="color: #ff4b4b; font-weight: bold;">{prob*100:.1f}%</span></h3>
-                    <p>Transaction flagged as high risk</p>
-                </div>
-                ''', unsafe_allow_html=True)
-            else:
-                st.markdown(f'''
-                <div class="legit-card">
-                    <h2>SECURE</h2>
-                    <h3>Confidence: <span style="color: #00ffcc; font-weight: bold;">{(1-prob)*100:.1f}%</span></h3>
-                    <p>Transaction appears legitimate</p>
-                </div>
-                ''', unsafe_allow_html=True)
+        if prob > 0.9898:
+            st.markdown(f'''
+            <div class="fraud-card">
+                <h2>FRAUD DETECTED</h2>
+                <h3>Confidence: <span style="color: #ff4b4b; font-weight: bold;">{prob*100:.1f}%</span></h3>
+                <p>Transaction flagged as high risk</p>
+            </div>
+            ''', unsafe_allow_html=True)
+        else:
+            st.markdown(f'''
+            <div class="legit-card">
+                <h2>SECURE</h2>
+                <h3>Confidence: <span style="color: #00ffcc; font-weight: bold;">{(1-prob)*100:.1f}%</span></h3>
+                <p>Transaction appears legitimate</p>
+            </div>
+            ''', unsafe_allow_html=True)
         
+        col1, col2 = st.columns(2)
         with col1:
             st.metric("Fraud Probability", f"{prob*100:.2f}%")
+        with col2:
             st.metric("Optimal Threshold", "98.98%")
-        
-        with col3:
-            st.json({
-                "amount": amt,
-                "merchant": merchant,
-                "category": category,
-                "distance_km": round(distance, 2),
-                "hour": dt_obj.hour,
-                "is_weekend": dt_obj.weekday() >= 5
-            })
             
     except Exception as e:
         st.error(f"Processing Error: {str(e)}")
-        st.error("Please check input formats and try again")
 
 st.sidebar.markdown("""
-<div style='text-align: center; padding: 20px;'>
+<div style='text-align: center; padding: 20px; background-color: #1e2130; border-radius: 10px;'>
     <h3>Model Performance</h3>
     <p><b>F1-Score:</b> 0.8350</p>
     <p><b>Precision:</b> 0.8706</p>
     <p><b>Recall:</b> 0.7995</p>
     <p><b>Threshold:</b> 0.9898</p>
 </div>
-""")
+""", unsafe_allow_html=True)
 
-st.sidebar.info("MoE model with 4 specialized experts for fraud detection.")
+st.sidebar.info("MoE model with 4 specialized experts for fraud detection.")