Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,62 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import joblib
+import os
+
+# ======================
+# LOAD MODEL
+# ======================
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+model = joblib.load(os.path.join(BASE_DIR, "breast_cancer_model.pkl"))
+
+# ======================
+# PAGE CONFIG
+# ======================
+st.set_page_config(
+    page_title="Breast Cancer Prediction",
+    page_icon="🎗️",
+    layout="centered"
+)
+
+st.title("🎗️ Breast Cancer Prediction")
+st.write("Predict whether a tumor is benign or malignant")
+
+# ======================
+# SIDEBAR INPUTS
+# ======================
+st.sidebar.header("Cell Nuclei Measurements")
+
+radius_mean = st.sidebar.slider("Radius Mean", 5.0, 30.0, 14.0)
+texture_mean = st.sidebar.slider("Texture Mean", 5.0, 40.0, 19.0)
+perimeter_mean = st.sidebar.slider("Perimeter Mean", 40.0, 200.0, 90.0)
+area_mean = st.sidebar.slider("Area Mean", 200.0, 2500.0, 650.0)
+smoothness_mean = st.sidebar.slider("Smoothness Mean", 0.05, 0.2, 0.1)
+
+# ======================
+# DATAFRAME
+# ======================
+input_df = pd.DataFrame({
+    "radius_mean": [radius_mean],
+    "texture_mean": [texture_mean],
+    "perimeter_mean": [perimeter_mean],
+    "area_mean": [area_mean],
+    "smoothness_mean": [smoothness_mean]
+})
 
-"""
-# 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)
+st.subheader("Input Data")
+st.write(input_df)
 
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
+# ======================
+# PREDICTION
+# ======================
+if st.button("Predict"):
 
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
+    prediction = model.predict(input_df)[0]
+    probability = model.predict_proba(input_df)[0][1]
 
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
+    st.subheader("Result")
 
-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])),
-    ))
+    if prediction == 1:
+        st.error(f"⚠️ Malignant Tumor ({probability:.2%})")
+    else:
+        st.success(f"✅ Benign Tumor ({1 - probability:.2%})")