Update app.py

app.py

@@ -18,6 +18,7 @@ from sklearn.ensemble import RandomForestClassifier
 from sklearn.naive_bayes import GaussianNB
 from sklearn.metrics import accuracy_score
 
+
 # Suppress TensorFlow warnings
 os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
@@ -56,19 +57,18 @@ def load_data():
     try:
         df = pd.read_csv("Training.csv")
         tr = pd.read_csv("Testing.csv")
-    except FileNotFoundError as e:
+    except FileNotFoundError:
         raise RuntimeError("Data files not found. Please ensure `Training.csv` and `Testing.csv` are uploaded correctly.")
-
+    
     disease_dict = {
-        # Example disease encoding dictionary, update accordingly
         'Fungal infection': 0, 'Allergy': 1, 'GERD': 2, 'Chronic cholestasis': 3, 'Drug Reaction': 4,
         'Peptic ulcer diseae': 5, 'AIDS': 6, 'Diabetes': 7, 'Gastroenteritis': 8, 'Bronchial Asthma': 9,
         'Hypertension': 10, 'Migraine': 11, 'Cervical spondylosis': 12, 'Paralysis': 13,
         'Jaundice': 14, 'Malaria': 15, 'Chicken pox': 16, 'Dengue': 17, 'Typhoid': 18,
         'Hepatitis A': 19, 'Hepatitis B': 20, 'Hepatitis C': 21, 'Hepatitis D': 22, 'Hepatitis E': 23,
         'Alcoholic hepatitis': 24, 'Tuberculosis': 25, 'Common Cold': 26, 'Pneumonia': 27,
-        'Heart attack': 29, 'Varicose veins': 30, 'Hypothyroidism': 31, 'Hyperthyroidism': 32,
-        'Hypoglycemia': 33, 'Osteoarthritis': 34, 'Arthritis': 35
+        'Heart attack': 28, 'Varicose veins': 29, 'Hypothyroidism': 30, 'Hyperthyroidism': 31,
+        'Hypoglycemia': 32, 'Osteoarthritis': 33, 'Arthritis': 34
     }
 
     df.replace({'prognosis': disease_dict}, inplace=True)
@@ -80,7 +80,7 @@ def load_data():
     return df, tr, disease_dict
 
 df, tr, disease_dict = load_data()
-l1 = list(df.columns[:-1])  
+l1 = list(df.columns[:-1])
 X = df[l1]
 y = df['prognosis']
 X_test = tr[l1]
@@ -107,19 +107,31 @@ def predict_disease(model, symptoms):
         if symptom in l1:
             input_test[l1.index(symptom)] = 1
     prediction = model.predict([input_test])[0]
-    return list(disease_dict.keys())[list(disease_dict.values()).index(prediction)]
+    confidence = model.predict_proba([input_test])[0][prediction] if hasattr(model, 'predict_proba') else None
+    return {
+        "disease": list(disease_dict.keys())[list(disease_dict.values()).index(prediction)],
+        "confidence": confidence
+    }
 
 def disease_prediction_interface(symptoms):
     symptoms_selected = [s for s in symptoms if s != "None"]
     
     if len(symptoms_selected) < 3:
-        return "Please select at least 3 symptoms for accurate prediction."
+        return ["Please select at least 3 symptoms for accurate prediction."]
     
     results = []
     for model_name, (model, acc) in trained_models.items():
-        prediction = predict_disease(model, symptoms_selected)
-        results.append(f"{model_name} Prediction: Predicted Disease: **{prediction}** (Accuracy: **{acc * 100:.2f}%**)")
-    
+        prediction_info = predict_disease(model, symptoms_selected)
+        predicted_disease = prediction_info["disease"]
+        confidence_score = prediction_info["confidence"]
+        
+        result = f"{model_name} Prediction: Predicted Disease: **{predicted_disease}**"
+        if confidence_score is not None:
+            result += f" (Confidence: {confidence_score:.2f})"
+        result += f" (Accuracy: {acc * 100:.2f}%)"
+        
+        results.append(result)
+
     return results
 
 # Helper Functions (for chatbot)
@@ -138,11 +150,7 @@ def generate_chatbot_response(message, history):
     try:
         result = chatbot_model.predict([bag_of_words(message, words)])
         tag = labels[np.argmax(result)]
-        response = "I'm sorry, I didn't understand that. 🤔"
-        for intent in intents_data["intents"]:
-            if intent["tag"] == tag:
-                response = random.choice(intent["responses"])
-                break
+        response = next((random.choice(intent["responses"]) for intent in intents_data["intents"] if intent["tag"] == tag), "I'm sorry, I didn't understand that. 🤔")
     except Exception as e:
         response = f"Error: {e}"
     history.append((message, response))