Update chest_utils.py

chest_utils.py

@@ -1,8 +1,47 @@
 import joblib
+import numpy as np
 
-model = joblib.load("chest_model.joblib")
+# Load the trained chest model
+chest_model = joblib.load("chest_model.joblib")
 
-def predict_chest(age, cough, fever, breathing):
-    features = [[age, cough == "Yes", fever == "Yes", breathing == "Yes"]]
-    return model.predict(features)[0]
+# 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
+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):
+    """
+    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'
+    """
+    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
+    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"