Update chest_utils.py

chest_utils.py

@@ -1,47 +1,50 @@
 import joblib
 import numpy as np
 
-# Load the trained chest model
+# 10 condition-based questions
+condition_questions = [
+    "Do you have a cough?",
+    "Do you feel shortness of breath?",
+    "Are you experiencing chest pain?",
+    "Do you smoke?",
+    "Do you have a fever?",
+    "Do you have fatigue?",
+    "Have you had recent respiratory infection?",
+    "Do you have a family history of lung issues?",
+    "Do you feel wheezing or noisy breathing?",
+    "Have you been exposed to pollution or chemicals recently?"
+]
+
+# Load the trained model
 chest_model = joblib.load("chest_model.joblib")
 
-# Define clinical questions mapped to conditions
-condition_questions = {
-    "Asthma": "Do you have shortness of breath or chest tightness?",
-    "Bronchiectasis": "Have you been experiencing chronic cough with sputum?",
-    "COPD": "Are you experiencing difficulty breathing and chronic cough?",
-    "Lung Cancer": "Have you noticed unexplained weight loss or coughing blood?",
-    "Pneumonia": "Do you have a fever, chills, and chest pain?",
-    "Tuberculosis": "Have you had a persistent cough for more than 3 weeks?"
-}
-
-# Helper encoding functions
+# Encoding helpers
 def encode_gender(gender):
     return 0 if gender == "Male" else 1
 
 def encode_view_position(position):
     return 0 if position == "PA" else 1
 
-# Predict function for combined model (with image not used in model itself)
-def predict_chest(image, age, gender, view_position, conditions):
+# Main prediction function
+def predict_chest(age, gender, view_position, conditions):
     """
     Parameters:
-    - image: uploaded X-ray (optional for future model use)
     - age: int
     - gender: 'Male' or 'Female'
     - view_position: 'PA' or 'AP'
     - conditions: list of 10 binary values (0 or 1)
 
     Returns:
-    - 'Chest Disease Detected' or 'No Chest Disease Detected'
+    - prediction string
     """
     gender_encoded = encode_gender(gender)
     position_encoded = encode_view_position(view_position)
-
+    
     if len(conditions) != 10:
         raise ValueError("Expected 10 binary values for conditions.")
 
-    # Combine all features into one row
+    # Combine all features
     features = np.array([[age, gender_encoded, position_encoded] + conditions])
-
     prediction = chest_model.predict(features)[0]
+    
     return "Chest Disease Detected" if prediction == 1 else "No Chest Disease Detected"