Update app.py

app.py

@@ -4,51 +4,191 @@ import tensorflow as tf
 import joblib
 
 # --- Load model and label encoder ---
-model = tf.keras.models.load_model("lung_cancer_classifier.h5")  # Load H5 model
+model = tf.keras.models.load_model("lung_cancer_classifier.h5")
 le_target = joblib.load("label_encoder.pkl")
 
 # --- Page configuration ---
-st.set_page_config(page_title="Lung Cancer Risk Classifier", layout="centered")
+st.set_page_config(
+    page_title="Lung Cancer Risk Classifier",
+    layout="centered"
+)
 
 # --- Title and description ---
 st.title("Lung Cancer Risk Prediction")
 st.write("""
-This application predicts the **level of lung cancer risk** for a patient.  
-**Note:** The data used to train this model consists of patients already diagnosed with lung cancer.  
-The target of this classification is the **Lung Cancer Level**.
+This application predicts the **level of lung cancer risk** for a patient.
+
+**Important:**  
+The model was trained using data from patients **already diagnosed** with lung cancer.  
+The output represents a **risk/severity level**, not a medical diagnosis.
 """)
 
 # --- Input fields ---
 Age = st.number_input("Age", min_value=1, max_value=100, value=37)
 
-# Gender dropdown fixed to show Male/Female but return encoded value
 Gender = st.selectbox(
     "Gender",
     options=[("Male", 1), ("Female", 2)],
-    format_func=lambda x: x[0]  # show only label
-)[1]  # get encoded value
-
-Air_Pollution = st.slider("Air Pollution Exposure", 1, 8, 4)
-Alcohol_use = st.slider("Alcohol Use", 1, 8, 5)
-Dust_Allergy = st.slider("Dust Allergy", 1, 8, 5)
-Occupational_Hazards = st.slider("Occupational Hazards", 1, 8, 5)
-Genetic_Risk = st.slider("Genetic Risk", 1, 7, 5)
-Chronic_Lung_Disease = st.slider("Chronic Lung Disease", 1, 7, 4)
-Balanced_Diet = st.slider("Balanced Diet", 1, 7, 4)
-Obesity = st.slider("Obesity", 1, 7, 4)
-Smoking = st.slider("Smoking", 1, 8, 4)
-Passive_Smoker = st.slider("Passive Smoker", 1, 8, 4)
-Chest_Pain = st.slider("Chest Pain", 1, 9, 4)
-Coughing_of_Blood = st.slider("Coughing of Blood", 1, 9, 4)
-Fatigue = st.slider("Fatigue", 1, 9, 4)
-Weight_Loss = st.slider("Weight Loss", 1, 8, 4)
-Shortness_of_Breath = st.slider("Shortness of Breath", 1, 9, 4)
-Wheezing = st.slider("Wheezing", 1, 8, 4)
-Swallowing_Difficulty = st.slider("Swallowing Difficulty", 1, 8, 4)
-Clubbing_of_Finger_Nails = st.slider("Clubbing of Finger Nails", 1, 9, 4)
-Frequent_Cold = st.slider("Frequent Cold", 1, 7, 4)
-Dry_Cough = st.slider("Dry Cough", 1, 7, 4)
-Snoring = st.slider("Snoring", 1, 7, 3)
+    format_func=lambda x: x[0]
+)[1]
+
+Air_Pollution = st.slider(
+    "Air Pollution Exposure",
+    1, 8, 4,
+    help="""
+    1 – Rural area
+    2 – Small town
+    3 – Urban residential
+    4 – Heavy traffic area
+    5 – Near industrial zone
+    6 – Factory worker
+    7 – High industrial exposure
+    8 – Extremely polluted environment
+    """
+)
+
+Alcohol_use = st.slider(
+    "Alcohol Use",
+    1, 8, 5,
+    help="""
+    1 – Never drinks
+    2 – Rare
+    3 – Occasional
+    4 – Social drinking
+    5 – Weekly
+    6 – Several times a week
+    7 – Daily
+    8 – Heavy drinker
+    """
+)
+
+Dust_Allergy = st.slider(
+    "Dust Allergy",
+    1, 8, 5,
+    help="1=None | 3=Mild | 5=Moderate | 7=Severe | 8=Chronic"
+)
+
+Occupational_Hazards = st.slider(
+    "Occupational Hazards",
+    1, 8, 5,
+    help="""
+    1 – Office / home work
+    3 – Outdoor non-industrial
+    5 – Factory work
+    6 – Chemical exposure
+    8 – Extreme industrial hazard
+    """
+)
+
+Genetic_Risk = st.slider(
+    "Genetic Risk",
+    1, 7, 5,
+    help="1=None | 3=Distant family history | 5=Immediate family | 7=Strong genetic risk"
+)
+
+Chronic_Lung_Disease = st.slider(
+    "Chronic Lung Disease",
+    1, 7, 4,
+    help="1=None | 3=Past condition | 5=Ongoing | 7=Severe"
+)
+
+Balanced_Diet = st.slider(
+    "Balanced Diet",
+    1, 7, 4,
+    help="1=Very poor | 3=Poor | 5=Mostly balanced | 7=Highly balanced"
+)
+
+Obesity = st.slider(
+    "Obesity",
+    1, 7, 4,
+    help="1=Normal | 3=Overweight | 5=Obese | 7=Severely obese"
+)
+
+Smoking = st.slider(
+    "Smoking",
+    1, 8, 4,
+    help="""
+    1 – Never smoked
+    3 – Former smoker
+    4 – Occasional smoker
+    5 – Light daily smoker
+    6 – Moderate smoker
+    7 – Heavy smoker
+    8 – Chain smoker
+    """
+)
+
+Passive_Smoker = st.slider(
+    "Passive Smoker",
+    1, 8, 4,
+    help="1=None | 3=Occasional | 5=Daily exposure | 8=Constant exposure"
+)
+
+Chest_Pain = st.slider(
+    "Chest Pain",
+    1, 9, 4,
+    help="1=None | 3=Mild | 5=Moderate | 7=Severe | 9=Extreme"
+)
+
+Coughing_of_Blood = st.slider(
+    "Coughing of Blood",
+    1, 9, 4,
+    help="1=None | 5=Occasional | 9=Frequent / severe"
+)
+
+Fatigue = st.slider(
+    "Fatigue",
+    1, 9, 4,
+    help="1=None | 3=Mild | 5=Moderate | 7=Severe | 9=Extreme"
+)
+
+Weight_Loss = st.slider(
+    "Weight Loss",
+    1, 8, 4,
+    help="1=None | 4=Noticeable | 6=Significant | 8=Severe"
+)
+
+Shortness_of_Breath = st.slider(
+    "Shortness of Breath",
+    1, 9, 4,
+    help="1=None | 3=Mild | 5=Moderate | 7=Severe | 9=Extreme"
+)
+
+Wheezing = st.slider(
+    "Wheezing",
+    1, 8, 4,
+    help="1=None | 4=Occasional | 6=Frequent | 8=Constant"
+)
+
+Swallowing_Difficulty = st.slider(
+    "Swallowing Difficulty",
+    1, 8, 4,
+    help="1=None | 4=Occasional | 6=Frequent | 8=Severe"
+)
+
+Clubbing_of_Finger_Nails = st.slider(
+    "Clubbing of Finger Nails",
+    1, 9, 4,
+    help="1=None | 5=Visible | 9=Severe"
+)
+
+Frequent_Cold = st.slider(
+    "Frequent Cold",
+    1, 7, 4,
+    help="1=Rare | 3=Occasional | 5=Frequent | 7=Very frequent"
+)
+
+Dry_Cough = st.slider(
+    "Dry Cough",
+    1, 7, 4,
+    help="1=None | 3=Mild | 5=Persistent | 7=Severe"
+)
+
+Snoring = st.slider(
+    "Snoring",
+    1, 7, 3,
+    help="1=None | 3=Occasional | 5=Frequent | 7=Severe"
+)
 
 # --- Combine inputs ---
 input_data = np.array([[
@@ -73,4 +213,4 @@ if st.button("Predict"):
 
 # --- Footer ---
 st.markdown("---")
-st.markdown("**Train By:** Edcel Bogay")
+st.markdown("**Trained By:** Edcel Bogay")