add shap value

interface.py

@@ -70,16 +70,16 @@ def predict_with_explanation(age, weight, height, gravidity, parity, h_abortion,
     if any(field is None or field == "" for field in required_fields):
         return "⚠️ لطفاً تمام فیلدها را پر کنید", "برای پیش‌بینی دقیق، تمام اطلاعات مورد نیاز است.", None
     
-    result, detailed_report = predict_outcome(
+    result, detailed_report, shap_plot = predict_outcome(
         age, weight, height, gravidity, parity, h_abortion, 
         living_child, gestational_age, hemoglobin, hematocrit, 
         platelet, mpv, pdw, neutrophil, lymphocyte
     )
     
-    return result, detailed_report
+    return result, detailed_report, shap_plot
 
 def clear_all_fields():
-    return tuple([None] * 17)
+    return tuple([None] * 17) + ("", "", None)
 
 def load_example(example_name):
     example_data = EXAMPLE_CASES[example_name]
@@ -103,6 +103,7 @@ def create_interface():
         - پیش‌بینی دقیق با استفاده از هوش مصنوعی
         - تحلیل SHAP برای توضیح تأثیر هر ویژگی
         - گزارش تفصیلی و قابل فهم برای پزشکان
+        - نمودار تصویری تأثیر پارامترها
         
         📝 **راهنما:** تمام فیلدها را پر کنید یا از مثال‌های آماده استفاده کنید.
         """)
@@ -119,6 +120,9 @@ def create_interface():
             with gr.Column(scale=2):
                 result_text = gr.Textbox(label="نتیجه پیش‌بینی", lines=2)
                 detailed_report = gr.Markdown(label="گزارش تفصیلی")
+            
+            with gr.Column(scale=1):
+                shap_plot = gr.Image(label="نمودار SHAP - تأثیر ویژگی‌ها", type="filepath")
         
         gr.Markdown("---")
         gr.Markdown("## 📚 مثال‌های آماده")
@@ -134,12 +138,12 @@ def create_interface():
         predict_btn.click(
             fn=predict_with_explanation,
             inputs=list(patient_inputs) + list(lab_inputs),
-            outputs=[result_text, detailed_report]
+            outputs=[result_text, detailed_report, shap_plot]
         )
         
         clear_btn.click(
             fn=clear_all_fields,
-            outputs=list(patient_inputs) + list(lab_inputs) + [result_text, detailed_report]
+            outputs=list(patient_inputs) + list(lab_inputs) + [result_text, detailed_report, shap_plot]
         )
     
     return demo

model_utils.py

@@ -1,10 +1,20 @@
 import numpy as np
 import joblib
 import warnings
-from config import MODEL_PATH
+import matplotlib.pyplot as plt
+import matplotlib
+import shap
+import os
+import tempfile
+from config import MODEL_PATH, FEATURE_NAMES
 
 warnings.filterwarnings('ignore')
 
+matplotlib.use('Agg')
+
+plt.rcParams['font.family'] = ['DejaVu Sans']
+plt.rcParams['axes.unicode_minus'] = False
+
 def calculate_derived_features(age, weight, height, neutrophil, lymphocyte, platelet):
     height_m = height / 100
     bmi = weight / (height_m ** 2)
@@ -12,11 +22,67 @@ def calculate_derived_features(age, weight, height, neutrophil, lymphocyte, plat
     plr = platelet / lymphocyte if lymphocyte > 0 else 0
     return bmi, nlr, plr
 
+def create_shap_plot(shap_values, feature_values, feature_names, prediction_proba):
+    shap_vals = shap_values[0][:, 1]  # Shape: (18,) - SHAP values for class 1
+        
+    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png')
+    temp_filename = temp_file.name
+    temp_file.close()
+    
+    fig, ax = plt.subplots(figsize=(10, 12))
+    
+    sorted_indices = np.argsort(np.abs(shap_vals))
+    sorted_shap_vals = shap_vals[sorted_indices]
+    sorted_feature_names = [feature_names[i] for i in sorted_indices]
+    sorted_feature_values = feature_values[sorted_indices]
+    
+    colors = ['red' if val > 0 else 'blue' for val in sorted_shap_vals]
+    bars = ax.barh(range(len(sorted_shap_vals)), sorted_shap_vals, color=colors, alpha=0.7)
+    
+    ax.set_yticks(range(len(sorted_feature_names)))
+    ax.set_yticklabels([f"{name} = {val:.2f}" for name, val in zip(sorted_feature_names, sorted_feature_values)])
+    ax.set_xlabel('SHAP Value (Impact on Prediction)', fontsize=12)
+    ax.set_title(f'Feature Impact Analysis\nComplication Risk: {prediction_proba[1]*100:.1f}%', 
+                fontsize=14, pad=20)
+    
+    ax.axvline(x=0, color='black', linestyle='-', alpha=0.3)
+    
+    for i, (bar, val) in enumerate(zip(bars, sorted_shap_vals)):
+        if val != 0:
+            ax.text(val + (0.001 if val > 0 else -0.001), i, f'{val:.3f}', 
+                    va='center', ha='left' if val > 0 else 'right', fontsize=9)
+    
+    ax.text(0.02, 0.98, 'Red: Increases risk\nBlue: Decreases risk', 
+            transform=ax.transAxes, va='top', ha='left', 
+            bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))
+    
+    plt.tight_layout()
+    plt.savefig(temp_filename, dpi=300, bbox_inches='tight', 
+                facecolor='white', edgecolor='none')
+    plt.close()
+    
+    return temp_filename
+
+def get_shap_explainer_and_values(model, input_data):
+        background_data = np.array([[
+            28, 65, 162, 24.7, 2, 1, 0, 1, 28, 11.5, 34.0, 
+            250, 8.5, 12.0, 6.0, 1.8, 3.33, 139
+        ]])
+        
+        explainer = shap.KernelExplainer(model.predict_proba, background_data)
+        shap_values = explainer.shap_values(input_data, nsamples=100)
+        
+        return shap_values
+
+        
 def predict_outcome(age, weight, height, gravidity, parity, h_abortion, 
                    living_child, gestational_age, hemoglobin, hematocrit, 
                    platelet, mpv, pdw, neutrophil, lymphocyte):
     model = get_model()
     
+    if model is None:
+        return "خطا: مدل بارگذاری نشده است", "", None
+    
     try:
         bmi, nlr, plr = calculate_derived_features(age, weight, height, neutrophil, lymphocyte, platelet)
         
@@ -48,13 +114,26 @@ def predict_outcome(age, weight, height, gravidity, parity, h_abortion,
 - NLR (نسبت نوتروفیل به لنفوسیت): {nlr:.2f}
 - PLR (نسبت پلاکت به لنفوسیت): {plr:.2f}
 
+**احتمالات تفصیلی:**
+- احتمال سالم بودن: {prediction_proba[0]*100:.1f}%
+- احتمال بروز عوارض: {prediction_proba[1]*100:.1f}%
+
 ⚠️ **توجه:** این پیش‌بینی صرفاً جهت کمک به تشخیص است و نباید جایگزین نظر پزشک شود.
         """
         
-        return result, detailed_report
+        shap_values = get_shap_explainer_and_values(model, input_data)
+        
+        shap_plot_path = create_shap_plot(
+            shap_values,
+            input_data[0], 
+            FEATURE_NAMES, 
+            prediction_proba
+        )
+
+        return result, detailed_report, shap_plot_path
         
     except Exception as e:
-        return f"خطا در پردازش: {str(e)}", ""
+        return f"خطا در پردازش: {str(e)}", "", None
 
 
 model = None
@@ -64,8 +143,21 @@ def get_model():
     if model is None:
         try:
             model = joblib.load(MODEL_PATH)
+            print("Model loaded successfully!")
             return model
         except Exception as e:
             print(f"Error loading model: {e}")
             return None
     return model
+
+def cleanup_temp_files():
+    try:
+        temp_dir = tempfile.gettempdir()
+        for filename in os.listdir(temp_dir):
+            if filename.endswith('.png') and 'tmp' in filename:
+                try:
+                    os.remove(os.path.join(temp_dir, filename))
+                except:
+                    pass
+    except:
+        pass