Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,76 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 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%}")
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# --- Footer ---
+st.markdown("---")
+st.markdown("**Train By:** Edcel Bogay")