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app.py

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+import streamlit as st
+import numpy as np
+import tensorflow as tf
+import joblib
+
+# --- Load model and label encoder ---
+model = tf.keras.models.load_model("lung_cancer_classifier.h5")  # Load H5 model
+le_target = joblib.load("label_encoder.pkl")
+
+# --- Page configuration ---
+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**.
+""")
+
+# --- 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)
+
+# --- Combine inputs ---
+input_data = np.array([[
+    Age, Gender, Air_Pollution, Alcohol_use, Dust_Allergy,
+    Occupational_Hazards, Genetic_Risk, Chronic_Lung_Disease,
+    Balanced_Diet, Obesity, Smoking, Passive_Smoker,
+    Chest_Pain, Coughing_of_Blood, Fatigue, Weight_Loss,
+    Shortness_of_Breath, Wheezing, Swallowing_Difficulty,
+    Clubbing_of_Finger_Nails, Frequent_Cold, Dry_Cough, Snoring
+]])
+
+# --- Prediction ---
+if st.button("Predict"):
+    prediction = model.predict(input_data)
+    predicted_class = prediction.argmax(axis=1)[0]
+    confidence = prediction.max(axis=1)[0]
+
+    label = le_target.inverse_transform([predicted_class])[0]
+
+    st.success(f"Predicted Lung Cancer Level: {label}")
+    st.info(f"Prediction Confidence: {confidence:.2%}")
+
+# --- Footer ---
+st.markdown("---")
+st.markdown("**Train By:** Edcel Bogay")

label_encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:71f60109d277dd7a7214745370e35402d31c5abdec368c7a7a259ee5bcc180be
+size 351

lung_cancer_classifier.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7444437d2b7d73ef8de823a92a9dc2de6c7353ed30a71539c0352489869d0a74
+size 64032