Update app.py

app.py

@@ -1,4 +1,3 @@
-
 import gradio as gr
 import pandas as pd
 import numpy as np
@@ -6,34 +5,61 @@ import joblib
 import shap
 import matplotlib
 import traceback
+import warnings
+from sklearn.metrics import accuracy_score, confusion_matrix
 
-# FORCE AGG BACKEND: Prevents thread-safety crashes and `main thread not in main loop` GUI errors in Hugging Face Spaces
+warnings.filterwarnings('ignore')
 matplotlib.use('Agg')
 import matplotlib.pyplot as plt
 
 # ==========================================
-# 1. LOAD PRODUCTION ASSETS
+# 1. LOAD TRAINED ARTIFACTS FROM COLAB MEMORY
 # ==========================================
+print("Loading Model Artifacts...")
 try:
-    model = joblib.load('ensemble_model.pkl')
+    best_model = joblib.load('ensemble_model.pkl')
     scaler = joblib.load('scaler.pkl')
     imputer = joblib.load('imputer.pkl')
     encoder = joblib.load('encoder.pkl')
-    FEATURES = joblib.load('feature_names.pkl')
+    FEATURE_NAMES = joblib.load('feature_names.pkl')
     cat_columns = joblib.load('cat_columns.pkl')
-    target_names = ['Negative', 'Malaria', 'SCA', 'Co-infection']
-    xgb_base = model.named_estimators_['xgb']
+    
+    # Extract XGBoost from StackingClassifier for SHAP explainability
+    xgb_base = best_model.named_estimators_['xgb']
     explainer = shap.TreeExplainer(xgb_base)
+    print("All artifacts loaded successfully.")
 except Exception as e:
-    raise RuntimeError(f"Error loading model artifacts. Ensure all .pkl files are uploaded to the Hugging Face Space. Details: {e}")
+    print(f"Error loading artifacts: {e}. Ensure the training script ran successfully.")
+
+target_names = ['Negative', 'Malaria', 'SCA', 'Co-infection']
 
 # ==========================================
-# 2. CORE INFERENCE LOGIC (Phase 4 Deliverable)
+# 2. CORE PROCESSING & PREDICTION LOGIC
 # ==========================================
-def process_and_predict(input_df):
-    for c in set(FEATURES) - set(input_df.columns): input_df[c] = np.nan
-    df_aligned = input_df[FEATURES].copy()
+
+def preprocess_input(input_df):
+    """Replicates the exact Feature Engineering & Preprocessing from Training"""
+    df = input_df.copy()
+    
+    # Feature Engineering
+    symptom_cols = ['fever', 'chills', 'headache', 'muscle_aches', 'fatigue', 
+                    'loss_of_appetite', 'jaundice', 'abdominal_pain', 'joint_pain', 
+                    'splenomegaly', 'pallor', 'lymphadenopathy']
+    
+    df['symptom_severity_score'] = df[[c for c in symptom_cols if c in df.columns]].sum(axis=1)
     
+    if 'age' in df.columns:
+        df['age_group'] = pd.cut(df['age'], bins=[-1, 5, 12, 55, 120], labels=[0, 1, 2, 3]).astype(float)
+        
+    if 'hb' in df.columns and 'wbc' in df.columns:
+        df['infection_anemia_ratio'] = df['wbc'] / (df['hb'] + 1e-5)
+
+    # Align with model input shapes
+    for c in set(FEATURE_NAMES) - set(df.columns): 
+        df[c] = np.nan
+    df_aligned = df[FEATURE_NAMES].copy()
+
+    # Categorical Encoding
     MISSING_STR = 'MISSING_CAT'
     if cat_columns:
         present_cats = [c for c in cat_columns if c in df_aligned.columns]
@@ -41,199 +67,332 @@ def process_and_predict(input_df):
             df_aligned[present_cats] = df_aligned[present_cats].astype(str).replace(['nan', 'None'], np.nan)
             df_aligned[present_cats] = df_aligned[present_cats].fillna(MISSING_STR)
             df_aligned[present_cats] = encoder.transform(df_aligned[present_cats])
+            
             for i, col in enumerate(cat_columns):
                 if col in present_cats and MISSING_STR in encoder.categories_[i]:
                     missing_code = list(encoder.categories_[i]).index(MISSING_STR)
                     df_aligned[col] = df_aligned[col].replace(missing_code, np.nan)
-        
+
     for col in df_aligned.columns:
-        try:
-            df_aligned[col] = pd.to_numeric(df_aligned[col])
-        except Exception:
-            pass
-        
-    X_imp = pd.DataFrame(imputer.transform(df_aligned), columns=FEATURES)
-    X_scaled = pd.DataFrame(scaler.transform(X_imp), columns=FEATURES)
+        df_aligned[col] = pd.to_numeric(df_aligned[col], errors='coerce')
+
+    # Impute and Scale
+    X_imp = pd.DataFrame(imputer.transform(df_aligned), columns=FEATURE_NAMES)
+    X_scaled = pd.DataFrame(scaler.transform(X_imp), columns=FEATURE_NAMES)
     
-    preds, probs = model.predict(X_scaled), model.predict_proba(X_scaled)
-    input_df['AI_Diagnosis'] = [target_names[p] for p in preds]
-    input_df['Confidence'] = [f"{max(pr)*100:.1f}%" for pr in probs]
+    return X_scaled
+
+def get_specific_coinfection_type(hb, retic, hb_decline, hb_s):
+    """Determines granular sub-type of Co-infection based on critical markers"""
+    if hb < 5.0:
+        return "Co-infection: Severe Hyperhemolytic Malarial Crisis"
+    elif retic > 8.0:
+        return "Co-infection: Acute Hemolytic Malarial Crisis"
+    elif hb_decline and hb_s > 0:
+        return "Co-infection: Rapidly Progressing Vaso-occlusive Malarial Crisis"
+    else:
+        return "Co-infection: Concurrent Malaria & Sickle Cell Crisis"
+
+def get_clinical_recs(diag, rule_triggered=None):
+    recs = f"### Clinical Decision Support Protocol\n\n"
     
-    return input_df, X_scaled, preds, probs, X_imp
+    if rule_triggered:
+        recs += f"**Critical Protocol Triggered:** *{rule_triggered}*\n\n"
+
+    if 'Malaria' in diag and 'Co-infection' not in diag:
+        recs += "**Protocol:** Initiate Artemisinin-based Combination Therapy (ACT) per WHO guidelines.\n"
+    elif diag == 'SCA':
+        recs += "**Protocol:** Administer IV Fluids, oxygen therapy, and comprehensive pain management.\n"
+    elif 'Co-infection' in diag:
+        recs += "**Urgent Protocol:** High risk of hyperhemolytic or severe vaso-occlusive crisis.\n"
+        recs += "- **Action:** Immediate admission to high-dependency unit. Initiate rapid intravenous antimalarials, aggressive hydration, and prepare for potential blood transfusion.\n"
+    else:
+        recs += "**Action:** Patient is currently negative for active Malaria and SCA crisis.\n"
+        recs += "- **Follow-up:** Screen for Typhoid, Dengue, or other viral infections if febrile symptoms persist.\n"
+
+    recs += "\n---\n### Diagnostic Context Notes\n"
+    recs += "- **Overlapping Symptoms:** Fever, Fatigue, Jaundice, Splenomegaly, and Headache *(Headache is uncommon in SCA unless accompanied by severe anemia, cerebral malaria, or stroke risk).* \n"
+    recs += "- **Co-infection Prevalences:** Key clinical indicators for Co-infection include Severe Pallor + Jaundice, High fever, Splenomegaly + malaria, and Extreme Reticulocyte (>8%) + malaria."
+    
+    return recs
 
-def generate_shap_plot(X_scaled, preds, index=0):
+def generate_shap_plot(X_scaled):
     try:
         shap_values = explainer.shap_values(X_scaled)
-        pred_idx = preds[index]
+        
         if isinstance(shap_values, list):
-            pat_shap = shap_values[pred_idx][index]
-            base_val = explainer.expected_value[pred_idx]
+            pat_shap = shap_values[3][0] 
+            base_val = explainer.expected_value[3]
+        elif len(shap_values.shape) == 3:
+            pat_shap = shap_values[0, :, 3] 
+            base_val = explainer.expected_value[3] if isinstance(explainer.expected_value, list) else explainer.expected_value
         else:
-            pat_shap = shap_values[index, :, pred_idx] if len(shap_values.shape)==3 else shap_values[index]
-            base_val = explainer.expected_value[pred_idx] if isinstance(explainer.expected_value, list) else explainer.expected_value
+            pat_shap = shap_values[0]
+            base_val = explainer.expected_value
 
-        fig, ax = plt.subplots(figsize=(6, 4))
+        fig, ax = plt.subplots(figsize=(7, 5))
+        ax.spines['top'].set_visible(False)
+        ax.spines['right'].set_visible(False)
+        
         explanation = shap.Explanation(values=pat_shap, base_values=base_val, 
-                                       data=X_scaled.iloc[index], feature_names=FEATURES)
+                                       data=X_scaled.iloc[0], feature_names=FEATURE_NAMES)
         shap.waterfall_plot(explanation, show=False)
-        plt.title(f"SHAP Force/Waterfall Explanation for Patient")
+        plt.title("XAI Feature Contribution (Impact on Co-Infection Risk)", fontsize=11, fontweight='bold')
         plt.tight_layout()
         return fig
     except Exception as e:
         fig, ax = plt.subplots(figsize=(6,4))
-        ax.text(0.5, 0.5, f"SHAP Error: {str(e)}", ha='center', va='center', color='red')
+        ax.text(0.5, 0.5, f"Interpretability Module Offline:\n{str(e)}", ha='center', va='center')
         return fig
 
-def extract_shap_dict(X_scaled, preds, index=0):
+def manual_inference(age, sex, temp, hb, wbc, platelets, hb_a, hb_s, hb_f, malaria_rdt, reticulocyte, hb_rapid_decline, 
+                     fever, chills, headache, muscle_aches, fatigue, loss_of_appetite, jaundice, abdominal_pain, joint_pain, splenomegaly, pallor, lymphadenopathy):
     try:
-        shap_values = explainer.shap_values(X_scaled)
-        pred_idx = preds[index]
-        if isinstance(shap_values, list):
-            pat_shap = shap_values[pred_idx][index]
-        else:
-            pat_shap = shap_values[index, :, pred_idx] if len(shap_values.shape)==3 else shap_values[index]
-        return {feat: impact for feat, impact in zip(FEATURES, pat_shap)}
-    except:
-        return None
-
-def get_clinical_recs(diag, raw_data, shap_values_dict=None):
-    base_str = ""
-    if shap_values_dict:
-        sorted_feats = sorted(shap_values_dict.items(), key=lambda item: abs(item[1]), reverse=True)[:3]
-        drivers = ", ".join([f"{feat.upper()} ({impact:+.2f})" for feat, impact in sorted_feats])
-        base_str = f"**Interpretability Output:** Prediction: {diag}. Key drivers: {drivers}\n\n"
-        
-    recs = base_str + f"**Clinical Decision Support for:** {diag}\n\n"
-    if diag == 'Malaria':
-        recs += "- **Protocol:** Initiate Artemisinin-based Combination Therapy (ACT).\n"
-        if raw_data.get('temp', 0) > 38.0: recs += "- **Vitals Alert:** High Fever. Administer antipyretics.\n"
-        if raw_data.get('hb', 12) < 8.0: recs += "- **Lab Alert:** Severe Anemia present. Prepare for blood transfusion review.\n"
-    elif diag == 'SCA':
-        recs += "- **Protocol:** Administer IV Fluids, oxygen therapy, and pain management.\n"
-        if raw_data.get('hb_s', 0) > 30: recs += "- **Lab Alert:** High HbS detected. Review Hydroxyurea therapy candidacy.\n"
-    elif diag == 'Co-infection':
-        recs += "- **URGENT PROTOCOL:** High risk of hyperhemolytic crisis.\n"
-        recs += "- **Action:** Admit to high-dependency unit. Initiate rapid antimalarials and aggressive hydration.\n"
-    else:
-        recs += "- **Action:** Negative for Malaria and SCA.\n"
-        recs += "- **Follow-up:** Screen for Typhoid, Dengue, or viral infections if symptoms persist.\n"
-    return recs
+        co_infection_flag = False
+        rule_triggered = ""
+        specific_coinfection_name = ""
+
+        # Hardcoded Critical Clinical Override Rules
+        if hb < 5.0:
+            co_infection_flag = True
+            rule_triggered = "Hemoglobin below critical threshold (5.0 g/dL)"
+        elif reticulocyte > 8.0 and malaria_rdt == "Positive":
+            co_infection_flag = True
+            rule_triggered = "Extreme Reticulocyte (>8%) + Positive Malaria RDT"
+        elif hb_rapid_decline and malaria_rdt == "Positive" and hb_s > 0:
+            co_infection_flag = True
+            rule_triggered = "Rapid Hb decline (>1.5g/dL in 48h) + Positive Malaria + SCA Genotype"
+
+        if co_infection_flag:
+            specific_coinfection_name = get_specific_coinfection_type(hb, reticulocyte, hb_rapid_decline, hb_s)
 
-# ==========================================
-# 3. GRADIO EVENT HANDLERS
-# ==========================================
-def manual_inference(age, sex, temp, hb, malaria_rdt, hb_s, wbc, platelets, fever, headache, jaundice):
-    try:
         input_data = pd.DataFrame({
-            'age': [age], 'sex': [sex], 'temp': [temp], 'hb': [hb], 
-            'malaria_rdt': [1.0 if malaria_rdt == "Positive" else 0.0], 
-            'hb_s': [hb_s], 'wbc': [wbc], 'platelets': [platelets],
-            'fever': [1.0 if fever else 0.0], 'headache': [1.0 if headache else 0.0], 'jaundice': [1.0 if jaundice else 0.0]
+            'age': [age], 'sex': [sex], 'temp': [temp], 'hb': [hb], 'wbc': [wbc], 'platelets': [platelets],
+            'hb_a': [hb_a], 'hb_s': [hb_s], 'hb_f': [hb_f],
+            'malaria_rdt': [1.0 if malaria_rdt == "Positive" else 0.0],
+            'reticulocyte': [reticulocyte], 'hb_rapid_decline': [1.0 if hb_rapid_decline else 0.0],
+            'fever': [1.0 if fever else 0.0], 'chills': [1.0 if chills else 0.0], 'headache': [1.0 if headache else 0.0],
+            'muscle_aches': [1.0 if muscle_aches else 0.0], 'fatigue': [1.0 if fatigue else 0.0], 
+            'loss_of_appetite': [1.0 if loss_of_appetite else 0.0], 'jaundice': [1.0 if jaundice else 0.0], 
+            'abdominal_pain': [1.0 if abdominal_pain else 0.0], 'joint_pain': [1.0 if joint_pain else 0.0], 
+            'splenomegaly': [1.0 if splenomegaly else 0.0], 'pallor': [1.0 if pallor else 0.0], 
+            'lymphadenopathy': [1.0 if lymphadenopathy else 0.0]
         })
+
+        X_scaled = preprocess_input(input_data)
+        probs = best_model.predict_proba(X_scaled)[0]
         
-        res_df, X_scaled, preds, probs, X_imp = process_and_predict(input_data)
-        
-        diag = res_df['AI_Diagnosis'].iloc[0]
-        conf = res_df['Confidence'].iloc[0]
-        raw_data = X_imp.iloc[0]
+        # Map probabilities to class names
+        prob_dict = {target_names[i]: probs[i] * 100 for i in range(len(target_names))}
         
-        shap_dict = extract_shap_dict(X_scaled, preds, 0)
-        recs = get_clinical_recs(diag, raw_data, shap_dict)
-        fig = generate_shap_plot(X_scaled, preds, 0)
+        # Apply Clinical Overrides if necessary
+        if co_infection_flag:
+            primary_diag = specific_coinfection_name
+            # Adjust probabilities to reflect the clinical override
+            prob_dict = {
+                specific_coinfection_name: 100.0,
+                'Malaria (Override)': prob_dict['Malaria'],
+                'SCA (Override)': prob_dict['SCA'],
+                'Negative': 0.0
+            }
+        else:
+            pred_idx = np.argmax(probs)
+            primary_diag = target_names[pred_idx]
+            
+            # If AI predicted co-infection without triggering rules, still give it a specific name
+            if primary_diag == 'Co-infection':
+                primary_diag = get_specific_coinfection_type(hb, reticulocyte, hb_rapid_decline, hb_s)
+                prob_dict[primary_diag] = prob_dict.pop('Co-infection')
+
+        # Formatting Output Markdown
+        diag_output = f"## Primary Diagnosis: {primary_diag}\n\n### Comprehensive Confidence Breakdown:\n"
         
-        return f"### **AI Diagnosis:** {diag}\n**Confidence:** {conf}", recs, fig
+        # Sort and display probabilities descending
+        sorted_probs = sorted(prob_dict.items(), key=lambda x: x[1], reverse=True)
+        for disease, conf in sorted_probs:
+            if 'Co-infection' in disease and 'Override' not in disease:
+                diag_output += f"- **{disease}**: {conf:.1f}%\n"
+            else:
+                diag_output += f"- **{disease}**: {conf:.1f}%\n"
+
+        recs = get_clinical_recs(primary_diag, rule_triggered)
+        fig = generate_shap_plot(X_scaled)
+
+        return diag_output, recs, fig
     except Exception as e:
-        error_msg = f"### ❌ Inference Error\n```\n{traceback.format_exc()}\n```"
-        return error_msg, "System Error. Please review inputs.", None
+        return f"### Inference Error\n```\n{traceback.format_exc()}\n```", "System Error.", None
 
-def batch_inference(file_path):
+# ==========================================
+# 3. SYSTEM VALIDATION HELPER FUNCTIONS
+# ==========================================
+
+def load_systematic_metrics():
     try:
-        if file_path is None:
-            return pd.DataFrame(), "### ⚠️ Upload Error: Please upload a valid CSV file.", "", None
-            
-        file_path_str = file_path if isinstance(file_path, str) else file_path.name
-        df = pd.read_csv(file_path_str)
+        y_test_val = joblib.load('y_test_val.pkl')
+        y_probs_val = joblib.load('y_probs_val.pkl')
+        y_pred_val = np.argmax(y_probs_val, axis=1)
+
+        acc = accuracy_score(y_test_val, y_pred_val)
+        cm = confusion_matrix(y_test_val, y_pred_val)
         
-        if df.empty:
-            return df, "### ⚠️ Data Error: The uploaded CSV is empty.", "", None
+        sens_list, spec_list = [], []
+        for i in range(len(cm)):
+            tp = cm[i,i]
+            fn = np.sum(cm[i,:]) - tp
+            fp = np.sum(cm[:,i]) - tp
+            tn = np.sum(cm) - tp - fn - fp
+            sens_list.append(tp / (tp + fn) if (tp + fn) > 0 else 0)
+            spec_list.append(tn / (tn + fp) if (tn + fp) > 0 else 0)
             
-        res_df, X_scaled, preds, probs, X_imp = process_and_predict(df)
-        
-        diag = res_df['AI_Diagnosis'].iloc[0]
-        conf = res_df['Confidence'].iloc[0]
-        raw_data = X_imp.iloc[0]
-        
-        shap_dict = extract_shap_dict(X_scaled, preds, 0)
-        recs = get_clinical_recs(diag, raw_data, shap_dict)
-        fig = generate_shap_plot(X_scaled, preds, 0)
-        
-        report_text = f"**Batch Processing Complete ({len(res_df)} records analyzed)**\n\n**Deep Dive Analysis (Patient 1):**\n**Diagnosis:** {diag} ({conf})"
-        
-        display_cols = ['AI_Diagnosis', 'Confidence'] + [c for c in FEATURES if c in res_df.columns]
-        return res_df[display_cols].head(15), report_text, recs, fig
+        sens = np.mean(sens_list)
+        spec = np.mean(spec_list)
+
+        return f"### Systematic Evaluation Metrics (Held-out Cohort)\n\n- **Overall Accuracy**: {acc*100:.2f}%\n- **Sensitivity (Macro)**: {sens*100:.2f}%\n- **Specificity (Macro)**: {spec*100:.2f}%"
+    except Exception as e:
+        return f"Error loading validation metrics: Ensure 'y_test_val.pkl' and 'y_probs_val.pkl' exist in memory. \n({str(e)})"
+
+def check_calibration(class_name):
+    try:
+        from sklearn.calibration import CalibrationDisplay
+        y_test_val = joblib.load('y_test_val.pkl')
+        y_probs_val = joblib.load('y_probs_val.pkl')
+        class_idx = target_names.index(class_name)
+
+        y_true_binary = (y_test_val == class_idx).astype(int)
+        y_prob_class = y_probs_val[:, class_idx]
+
+        fig, ax = plt.subplots(figsize=(6, 5))
+        ax.spines['top'].set_visible(False)
+        ax.spines['right'].set_visible(False)
+        CalibrationDisplay.from_predictions(y_true_binary, y_prob_class, n_bins=10, ax=ax, name=class_name)
+        plt.title(f"Reliability Curve (Calibration) for {class_name}", fontweight='bold')
+        plt.tight_layout()
+        return fig
     except Exception as e:
-        error_msg = f"### ❌ Batch Error\n```\n{traceback.format_exc()}\n```"
-        return pd.DataFrame(), error_msg, "Failed to process batch. Ensure CSV schema matches training format.", None
+        fig, ax = plt.subplots()
+        ax.text(0.5, 0.5, f"Calibration Error:\n{str(e)}", ha='center')
+        return fig
 
 # ==========================================
-# 4. HUGGING FACE GRADIO UI CONSTRUCTION
+# 4. GRADIO UI DEFINITION
 # ==========================================
-with gr.Blocks(theme=gr.themes.Monochrome(), title="Hemaclass XAI Dashboard") as demo:
-    gr.Markdown("# 🏥 Hemaclass Clinical Decision Support Dashboard")
-    gr.Markdown("Phase 4 Prototype: Explainable AI-Driven Ensemble Model for Malaria and Sickle Cell Anemia Classification in Western Kenya.")
-    
+
+custom_theme = gr.themes.Monochrome(
+    primary_hue="slate",
+    secondary_hue="gray",
+    font=[gr.themes.GoogleFont("Inter"), "ui-sans-serif", "system-ui", "sans-serif"]
+)
+
+# 10 Detailed Clinical Examples spanning all feature variations
+clinical_examples = [
+    # [age, sex, temp, hb, wbc, platelets, hb_a, hb_s, hb_f, rdt, retic, hb_decline, fever, chills, headache, muscle, fatigue, appetite, jaundice, abd_pain, joint_pain, spleno, pallor, lymph]
+    [8, "Male", 39.5, 11.5, 9.5, 150, 98.0, 0.0, 2.0, "Positive", 1.5, False, True, True, True, True, True, True, False, False, False, False, False, False],  # 1. Uncomplicated Malaria
+    [22, "Female", 39.0, 7.5, 12.0, 90, 95.0, 0.0, 2.0, "Positive", 4.0, False, True, True, True, True, True, True, True, False, False, True, True, False],     # 2. Severe Malaria
+    [15, "Male", 37.2, 8.0, 11.0, 250, 5.0, 85.0, 10.0, "Negative", 6.0, False, False, False, False, True, True, False, True, True, True, False, True, False],  # 3. SCA Vaso-occlusive Crisis
+    [18, "Female", 37.5, 4.5, 14.0, 300, 2.0, 90.0, 8.0, "Negative", 10.0, True, False, False, False, False, True, False, True, False, True, True, True, False], # 4. SCA Hyperhemolytic (Trigger Hb<5)
+    [12, "Male", 38.8, 6.5, 16.0, 110, 10.0, 80.0, 10.0, "Positive", 9.5, False, True, True, True, True, True, True, True, True, True, True, True, False],     # 5. Co-infection (Acute Hemolytic, Retic>8)
+    [25, "Female", 39.2, 7.0, 15.0, 100, 5.0, 85.0, 10.0, "Positive", 5.0, True, True, True, True, True, True, True, True, False, True, True, True, False],    # 6. Co-infection (Rapidly Progressing)
+    [30, "Male", 36.8, 14.0, 6.5, 250, 98.0, 0.0, 2.0, "Negative", 1.0, False, False, False, False, False, False, False, False, False, False, False, False, False], # 7. Healthy Adult
+    [45, "Female", 37.8, 13.5, 5.0, 210, 97.0, 0.0, 2.0, "Negative", 1.2, False, True, False, True, True, True, False, False, False, False, False, False, True], # 8. Viral Infection (Non-malarial)
+    [10, "Male", 39.8, 6.0, 18.0, 80, 95.0, 0.0, 3.0, "Positive", 7.0, False, True, True, True, False, True, True, True, True, False, True, True, False],      # 9. Malaria with Overlapping Symptoms
+    [28, "Female", 37.0, 12.5, 7.0, 220, 60.0, 38.0, 2.0, "Negative", 1.5, False, False, False, False, False, False, False, False, False, False, False, False, False] # 10. SCA Trait (Asymptomatic)
+]
+
+with gr.Blocks(theme=custom_theme, title="Hemaclass Clinical Dashboard") as demo:
+    gr.Markdown("# Hemaclass Clinical Decision Support System")
+    gr.Markdown("Deep Stacking Ensemble Model for Malaria and Sickle Cell Anemia Classification.")
+
     with gr.Tabs():
-        with gr.TabItem("🩺 Single Patient Evaluation"):
+        # --- TAB 1: CORE INFERENCE ---
+        with gr.TabItem("Single Patient Validation"):
             with gr.Row():
                 with gr.Column(scale=1):
-                    gr.Markdown("### Patient Demographics & Vitals")
-                    age_in = gr.Number(label="Patient Age", value=25)
-                    sex_in = gr.Dropdown(["Male", "Female"], label="Sex", value="Female")
-                    temp_in = gr.Slider(minimum=34.0, maximum=42.0, value=37.5, label="Body Temperature (°C)")
-                    
-                    gr.Markdown("### Critical Labs")
-                    hb_in = gr.Number(label="Hemoglobin (Hb) g/dL", value=12.0)
-                    rdt_in = gr.Radio(["Negative", "Positive"], label="Malaria RDT Result", value="Negative")
-                    hbs_in = gr.Slider(minimum=0.0, maximum=100.0, value=0.0, label="Hb S Fraction (%)")
-                    wbc_in = gr.Number(label="WBC Count (x10^9/L)", value=8.0)
-                    platelets_in = gr.Number(label="Platelets (x10^9/L)", value=200)
-                    
+                    gr.Markdown("### Demographics & Vitals")
+                    with gr.Row():
+                        age_in = gr.Number(label="Age", value=25)
+                        sex_in = gr.Dropdown(["Male", "Female"], label="Sex", value="Female")
+                        temp_in = gr.Number(label="Temperature (°C)", value=37.5)
+
                     gr.Markdown("### Clinical Symptoms")
-                    fever_in = gr.Checkbox(label="Fever")
-                    headache_in = gr.Checkbox(label="Headache")
-                    jaundice_in = gr.Checkbox(label="Jaundice")
-                    
-                    manual_btn = gr.Button("Evaluate Patient", variant="primary")
-                
-                with gr.Column(scale=2):
-                    gr.Markdown("### AI Prediction & Explainability")
                     with gr.Row():
-                        out_diag = gr.Markdown()
+                        fever_in = gr.Checkbox(label="Fever")
+                        chills_in = gr.Checkbox(label="Chills")
+                        headache_in = gr.Checkbox(label="Headache")
+                        fatigue_in = gr.Checkbox(label="Fatigue")
                     with gr.Row():
-                        out_recs = gr.Markdown()
+                        jaundice_in = gr.Checkbox(label="Jaundice")
+                        splenomegaly_in = gr.Checkbox(label="Splenomegaly")
+                        pallor_in = gr.Checkbox(label="Severe Pallor")
+                        muscle_in = gr.Checkbox(label="Muscle Aches")
+                    with gr.Accordion("Additional Symptoms", open=False):
+                        loss_appetite_in = gr.Checkbox(label="Loss of Appetite")
+                        abd_pain_in = gr.Checkbox(label="Abdominal Pain")
+                        joint_pain_in = gr.Checkbox(label="Joint Pain")
+                        lymph_in = gr.Checkbox(label="Lymphadenopathy")
+
+                    gr.Markdown("### Critical Laboratory Markers")
                     with gr.Row():
-                        out_shap = gr.Plot(label="Local Interpretability (SHAP Waterfall)")
-            
-            manual_btn.click(manual_inference, inputs=[age_in, sex_in, temp_in, hb_in, rdt_in, hbs_in, wbc_in, platelets_in, fever_in, headache_in, jaundice_in], outputs=[out_diag, out_recs, out_shap])
-            
-        with gr.TabItem("📂 Retrospective Batch Evaluation"):
-            gr.Markdown("Upload anonymized, cleaned clinical data (CSV) for batch processing and population-level analysis.")
-            file_in = gr.File(label="Upload Cleaned Clinical Dataset", type="filepath", file_types=[".csv"])
-            batch_btn = gr.Button("Run Batch Inference", variant="primary")
+                        rdt_in = gr.Radio(["Negative", "Positive"], label="Malaria RDT", value="Negative")
+                        retic_in = gr.Number(label="Reticulocyte Count (%)", value=2.0)
+                    with gr.Row():
+                        hb_in = gr.Number(label="Hemoglobin (g/dL)", value=12.0)
+                        hb_decline_in = gr.Checkbox(label="Rapid Hb Decline (>1.5g/dl in 48h)")
+                    with gr.Row():
+                        hb_a_in = gr.Number(label="HbA Fraction (%)", value=98.0)
+                        hb_s_in = gr.Number(label="HbS Fraction (%)", value=0.0)
+                        hb_f_in = gr.Number(label="HbF Fraction (%)", value=2.0)
+                    with gr.Row():
+                        wbc_in = gr.Number(label="WBC Count (x10^9/L)", value=8.0)
+                        platelets_in = gr.Number(label="Platelet Count", value=200)
+
+                    manual_btn = gr.Button("Validate Diagnosis", variant="primary", size="lg")
+
+                with gr.Column(scale=1):
+                    gr.Markdown("### System Output")
+                    out_diag = gr.Markdown()
+                    out_recs = gr.Markdown()
+                    out_shap = gr.Plot(label="Feature Contribution Analysis")
+                    
+            gr.Markdown("---")
+            gr.Markdown("### Load Clinical Scenarios")
+            gr.Markdown("Select a predefined clinical case to auto-populate the diagnostic fields.")
             
-            out_df = gr.Dataframe(label="Evaluation Results (Preview Top 15 rows)")
+            input_components = [
+                age_in, sex_in, temp_in, hb_in, wbc_in, platelets_in, hb_a_in, hb_s_in, hb_f_in, 
+                rdt_in, retic_in, hb_decline_in, fever_in, chills_in, headache_in, muscle_in, 
+                fatigue_in, loss_appetite_in, jaundice_in, abd_pain_in, joint_pain_in, 
+                splenomegaly_in, pallor_in, lymph_in
+            ]
             
-            gr.Markdown("---")
-            gr.Markdown("### Patient 1 XAI Deep Dive Analysis")
+            gr.Examples(
+                examples=clinical_examples,
+                inputs=input_components,
+                label="Predefined Patient Cases"
+            )
+
+            manual_btn.click(
+                manual_inference, 
+                inputs=input_components, 
+                outputs=[out_diag, out_recs, out_shap]
+            )
+
+        # --- TAB 2: PERFORMANCE METRICS ---
+        with gr.TabItem("Systematic Testing"):
+            gr.Markdown("### Overall Model Performance on Unseen Test Cohort")
+            metrics_btn = gr.Button("Calculate Systematic Metrics", variant="secondary")
+            out_metrics = gr.Markdown()
+            metrics_btn.click(load_systematic_metrics, inputs=[], outputs=[out_metrics])
+
+        # --- TAB 3: ADVANCED CALIBRATION ---
+        with gr.TabItem("Advanced Validation"):
+            gr.Markdown("### Evaluate Diagnosis Calibration")
+            gr.Markdown("Select a disease class below to verify the alignment between predicted probabilities and true clinical frequencies.")
             with gr.Row():
-                with gr.Column():
-                    batch_diag = gr.Markdown()
-                    batch_recs = gr.Markdown()
-                with gr.Column():
-                    batch_shap = gr.Plot(label="Patient 1 SHAP Interpretation")
-                    
-            batch_btn.click(batch_inference, inputs=file_in, outputs=[out_df, batch_diag, batch_recs, batch_shap])
+                class_dropdown = gr.Dropdown(target_names, label="Select Target Class", value="Co-infection")
+                calib_btn = gr.Button("Check Calibration", variant="secondary")
+            out_calib = gr.Plot()
+            calib_btn.click(check_calibration, inputs=[class_dropdown], outputs=[out_calib])
 
+# Launch inside Colab
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
-
+    demo.launch(share=True)