Update src/streamlit_app.py

src/streamlit_app.py

@@ -13,9 +13,10 @@ st.set_page_config(page_title="FinTech Fraud Guard", layout="wide")
 st.markdown("""
     <style>
     .main { background-color: #0e1117; }
-    .stButton>button { width: 100%; border-radius: 20px; background: linear-gradient(45deg, #ff4b4b, #ff8f8f); color: white; border: none; }
-    .fraud-card { padding: 20px; border-radius: 15px; background-color: #1e2130; border-left: 5px solid #ff4b4b; margin-bottom: 20px; }
-    .legit-card { padding: 20px; border-radius: 15px; background-color: #1e2130; border-left: 5px solid #00ffcc; margin-bottom: 20px; }
+    .stButton>button { width: 100%; border-radius: 20px; background: linear-gradient(45deg, #ff4b4b, #ff8f8f); color: white; border: none; font-weight: bold; }
+    .fraud-card { padding: 25px; border-radius: 15px; background-color: #1e2130; border-left: 8px solid #ff4b4b; margin-bottom: 20px; box-shadow: 0 4px 15px rgba(255, 75, 75, 0.2); }
+    .legit-card { padding: 25px; border-radius: 15px; background-color: #1e2130; border-left: 8px solid #00ffcc; margin-bottom: 20px; box-shadow: 0 4px 15px rgba(0, 255, 204, 0.1); }
+    .stTextInput>div>div>input, .stNumberInput>div>div>input { background-color: #262730; color: white; border-radius: 10px; }
     </style>
     """, unsafe_allow_html=True)
 
@@ -94,16 +95,12 @@ class MoEFraudModel(nn.Module):
     def forward(self, cat_input, num_input):
         all_embs = [self.embeddings[c](cat_input[:, i]) for i, c in enumerate(self.cat_cols)]
         global_features = torch.cat([torch.cat(all_embs, dim=1), num_input], dim=1)
-        
         weights = self.gating_network(global_features)
-        
         h1 = self.expert1(self.get_features(cat_input, num_input, self.e1_cols_cat, self.e1_cols_num))
         h2 = self.expert2(self.get_features(cat_input, num_input, self.e2_cols_cat, self.e2_cols_num))
         h3 = self.expert3(self.get_features(cat_input, num_input, self.e3_cols_cat, self.e3_cols_num))
         h4 = self.expert4(self.get_features(cat_input, num_input, self.e4_cols_cat, self.e4_cols_num))
-        
         h_final = (weights[:, 0:1]*h1 + weights[:, 1:2]*h2 + weights[:, 2:3]*h3 + weights[:, 3:4]*h4)
-        
         return self.classifier(h_final)
 
 REPO_ID = "rocky250/FinTech"
@@ -114,82 +111,64 @@ def load_assets():
     config_path = hf_hub_download(repo_id=REPO_ID, filename="config.json")
     encoders_path = hf_hub_download(repo_id=REPO_ID, filename="label_encoders.joblib")
     scaler_path = hf_hub_download(repo_id=REPO_ID, filename="scaler.joblib")
-    
     with open(config_path, 'r') as f:
         config = json.load(f)
-    
     model = MoEFraudModel(config['cat_dims'], config['num_cols_map'], config['embed_dim'])
     model.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu')))
     model.eval()
-    
     return model, joblib.load(encoders_path), joblib.load(scaler_path), config
 
 model, encoders, scaler, config = load_assets()
 
-st.title("FinTech Fraud Analysis")
-st.markdown("### Proposed Mixture-of-Experts (MoE) Architecture")
+st.title("FinTech Mixture-of-Experts Fraud Guard")
+st.markdown("---")
 
-with st.form("transaction_form"):
-    col1, col2, col3 = st.columns(3)
+with st.form("main_form"):
+    c1, c2 = st.columns(2)
     
-    with col1:
-        st.subheader("Customer Info")
-        first = st.text_input("First Name", "Jeff")
-        last = st.text_input("Last Name", "Elliott")
+    with c1:
+        st.subheader("Transaction Details")
+        trans_dt = st.text_input("Transaction Date & Time", value="21-06-2020 12:14")
+        amt = st.number_input("Transaction Amount ($)", value=2.86, format="%.2f")
+        cc_num = st.text_input("Credit Card Number", value="2291160000000000")
+        merchant = st.text_input("Merchant Name", value="fraud_Kirlin and Sons")
+        category = st.text_input("Category", value="personal_care")
+        
+    with c2:
+        st.subheader("👤 Cardholder Info")
+        col_a, col_b = st.columns(2)
+        first_name = col_a.text_input("First Name", value="Jeff")
+        last_name = col_b.text_input("Last Name", value="Elliott")
         gender = st.selectbox("Gender", ["M", "F"])
-        cc_num = st.text_input("Credit Card Number", "2.29116E+15")
-        
-    with col2:
-        st.subheader("Transaction Info")
-        trans_dt = st.text_input("Date & Time (DD-MM-YYYY HH:MM)", "21-06-2020 12:14")
-        merchant = st.text_input("Merchant", "fraud_Kirlin and Sons")
-        category = st.text_input("Category", "personal_care")
-        amt = st.number_input("Amount ($)", value=2.86)
-        
-    with col3:
-        st.subheader("Location Info")
-        street = st.text_input("Street", "351 Darlene Green")
-        city = st.text_input("City", "Birmingham")
-        state = st.text_input("State", "AL")
-        zip_code = st.text_input("Zip Code", "35201")
+        street = st.text_input("Street Address", value="351 Darlene Green")
+        city = st.text_input("City", value="Birmingham")
+        state = st.text_input("State", value="AL")
+        zip_code = st.text_input("Zip Code", value="35201")
+        job = st.text_input("Job Title", value="Software Engineer")
 
-    submit = st.form_submit_button("ANALYZE TRANSACTION")
+    submit = st.form_submit_button("RUN FRAUD ANALYSIS")
 
 if submit:
     try:
         dt_obj = datetime.strptime(trans_dt, "%d-%m-%Y %H:%M")
         
         num_feats = [
-            amt, 
-            dt_obj.hour, 
-            dt_obj.weekday(), 
-            1 if dt_obj.weekday() >= 5 else 0, 
-            dt_obj.timestamp(),
-            50000, 
-            35, 
-            3600, 
-            50.0, 
-            10.0, 
-            100.0, 
-            0.02, 
-            1000, 
-            33.5, 
-            -86.8, 
-            33.6, 
-            -86.9, 
-            15.5, 
-            0
+            amt, dt_obj.hour, dt_obj.weekday(), 
+            1 if dt_obj.weekday() >= 5 else 0, dt_obj.timestamp(),
+            50000, 35, 3600, 50.0, 10.0, 100.0, 
+            0.02, 1000, 33.5, -86.8, 33.6, -86.9, 15.5, 0
         ]
         
-        cat_feats = [category, cc_num, gender, "Job", city, state, zip_code, merchant]
+        cat_order = ['category', 'cc_num', 'gender', 'job', 'city', 'state', 'zip', 'merchant']
+        cat_vals = [category, cc_num, gender, job, city, state, zip_code, merchant]
         
         num_input = scaler.transform([num_feats])
         
         cat_encoded = []
-        for i, col in enumerate(config['cat_dims'].keys()):
-            val = str(cat_feats[i])
-            if val in encoders[col].classes_:
-                cat_encoded.append(encoders[col].transform([val])[0])
+        for i, col_name in enumerate(cat_order):
+            val = str(cat_vals[i])
+            if val in encoders[col_name].classes_:
+                cat_encoded.append(encoders[col_name].transform([val])[0])
             else:
                 cat_encoded.append(0)
         
@@ -199,21 +178,30 @@ if submit:
             logits = model(cat_t, num_t)
             prob = torch.sigmoid(logits).item()
 
-        st.divider()
+        st.markdown("### Analysis Result")
         if prob > 0.5:
-            st.markdown(f"""<div class="fraud-card">
-                <h2>High Risk Detected!</h2>
-                <p>Confidence: {prob*100:.2f}%</p>
-                <p>This transaction matches fraud patterns identified by the Expert Gating Network.</p>
-                </div>""", unsafe_allow_html=True)
+            st.markdown(f"""
+                <div class="fraud-card">
+                    <h2 style="color:#ff4b4b; margin-top:0;">High Risk Detected</h2>
+                    <p style="font-size:1.2rem;">Fraud Probability: <strong>{prob*100:.2f}%</strong></p>
+                    <hr style="border:0.5px solid #ff4b4b33;">
+                    <p>The MoE Gating Network has flagged this transaction as highly suspicious based on merchant patterns and transaction timing.</p>
+                </div>
+                """, unsafe_allow_html=True)
         else:
-            st.markdown(f"""<div class="legit-card">
-                <h2>Transaction Safe</h2>
-                <p>Confidence: {(1-prob)*100:.2f}%</p>
-                <p>Analysis shows this is a legitimate transaction based on customer behavior Experts.</p>
-                </div>""", unsafe_allow_html=True)
+            st.markdown(f"""
+                <div class="legit-card">
+                    <h2 style="color:#00ffcc; margin-top:0;">Transaction Verified</h2>
+                    <p style="font-size:1.2rem;">Fraud Probability: <strong>{prob*100:.2f}%</strong></p>
+                    <hr style="border:0.5px solid #00ffcc33;">
+                    <p>Our experts have analyzed the spatial and behavioral features and determined this transaction is likely legitimate.</p>
+                </div>
+                """, unsafe_allow_html=True)
                 
     except Exception as e:
-        st.error(f"Error in processing: {e}")
+        st.error(f"Validation Error: Ensure Date Format is DD-MM-YYYY HH:MM. Raw Error: {e}")
 
-st.sidebar.info("This model uses a MoE Architecture with 4 specialized experts for Financial Fraud Detection.")
+st.sidebar.title("System Info")
+st.sidebar.info("Model: MoE Neural Network")
+st.sidebar.write(f"Repository: {REPO_ID}")
+st.sidebar.write("Architecture: 4 Experts (Temporal, Behavioral, Frequency, Spatial)")