Initial Commit

app.py

@@ -64,7 +64,10 @@ class_index_mapping = {i: label for i, label in enumerate(class_labels)}
 # Create a bar chart to represent symptom weightage
 def show_symptom_weightage(symptoms, weights):
     plt.figure(figsize=(10, 6))
-    plt.bar(symptoms, weights)
+    # Filter out the non-selected symptoms and their corresponding weights
+    selected_symptoms = [symptom for symptom, weight in zip(symptoms, weights) if weight > 0]
+    selected_weights = [weight for weight in weights if weight > 0]
+    plt.bar(selected_symptoms, selected_weights)
     plt.xlabel("Symptom")
     plt.ylabel("Weight")
     plt.title("Weightage/Importance of Symptoms")
@@ -164,20 +167,14 @@ def predict_covid(*args):
 #     text_prob_positive = ((logreg_prob_positive + rf_prob_positive + dt_prob_positive +
 #                           knn_prob_positive + svm_prob_positive + ann_prob_positive) / 6) * 100
     
-    # weights = [1.0] * len(symptoms) 
-    # probabilities = [logreg_prob_positive, rf_prob_positive, dt_prob_positive,
-    #                  knn_prob_positive, svm_prob_positive, ann_prob_positive]
-
-    # # Manually calculate the weighted average
-    # total_weighted_prob = sum(prob * weight for prob, weight in zip(probabilities, weights))
-    # total_weight = sum(weights)
-    # text_prob_positive = (total_weighted_prob / total_weight) * 100 if total_weight != 0 else 0.0
-
-    weights = [0.2, 0.2, 0.2, 0.2, 0.1, 0.2]  # Example weights for each model
+    weights = [1.0 if symptom else 0.0 for symptom in symptoms]
     probabilities = [logreg_prob_positive, rf_prob_positive, dt_prob_positive,
                      knn_prob_positive, svm_prob_positive, ann_prob_positive]
 
-    text_prob_positive = np.average(probabilities, weights=weights) * 100
+    # Manually calculate the weighted average
+    total_weighted_prob = sum(prob * weight for prob, weight in zip(probabilities, weights))
+    total_weight = sum(weights)
+    text_prob_positive = (total_weighted_prob / total_weight) * 100 if total_weight != 0 else 0.0
 
     # Visualize the symptom weightage/importance    
     symptom_weightage_img = show_symptom_weightage(column_names[:-1], weights)