Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,24 +1,17 @@
 import streamlit as st
-import pandas as pd
-import matplotlib.pyplot as plt
-
 
 # ---------------------------------------------------
-# PAGE CONFIG
+# PAGE SETTINGS
 # ---------------------------------------------------
 
 st.set_page_config(
-    page_title="Île-de-France Housing Price Estimator",
+    page_title="Housing Price Estimator",
     page_icon="🏠",
-    layout="wide"
+    layout="centered"
 )
 
 # ---------------------------------------------------
-# LOAD DATA
-# ---------------------------------------------------
-
-# ---------------------------------------------------
-# MODEL COEFFICIENTS (from R)
+# MODEL COEFFICIENTS (from R regression)
 # ---------------------------------------------------
 
 INTERCEPT = -467418.10
@@ -40,43 +33,43 @@ def predict_price(surface, rooms, neighborhood_score):
 
 
 # ---------------------------------------------------
-# TITLE
+# APP TITLE
 # ---------------------------------------------------
 
 st.title("🏠 Île-de-France Housing Price Estimator")
 
 st.write(
 """
-This dashboard estimates housing prices using a **linear regression model**
-trained on the **DVF real estate dataset**.
+Estimate the value of a property using a **linear regression model**
+trained on real estate data from the DVF dataset.
 """
 )
 
 # ---------------------------------------------------
-# SIDEBAR INPUTS
+# USER INPUTS
 # ---------------------------------------------------
 
-st.sidebar.header("Property characteristics")
+st.sidebar.header("Property Information")
 
 surface = st.sidebar.slider(
     "Surface (m²)",
-    20,
-    300,
-    70
+    min_value=20,
+    max_value=300,
+    value=70
 )
 
 rooms = st.sidebar.slider(
     "Number of rooms",
-    1,
-    10,
-    3
+    min_value=1,
+    max_value=10,
+    value=3
 )
 
 neighborhood_score = st.sidebar.slider(
     "Neighborhood score",
-    3.0,
-    10.0,
-    7.0
+    min_value=3.0,
+    max_value=10.0,
+    value=7.0
 )
 
 # ---------------------------------------------------
@@ -93,68 +86,26 @@ if st.sidebar.button("Estimate price"):
 
 
 # ---------------------------------------------------
-# DATASET OVERVIEW
-# ---------------------------------------------------
-
-st.write("---")
-st.header("Dataset Overview")
-
-col1, col2, col3 = st.columns(3)
-
-col1.metric("Number of properties", len(df))
-col2.metric("Average price", f"{int(df['price'].mean()):,} €")
-col3.metric("Average price per m²", f"{int(df['price_m2'].mean()):,} €")
-
-
-# ---------------------------------------------------
-# PRICE DISTRIBUTION
-# ---------------------------------------------------
-
-st.write("---")
-st.header("Price Distribution")
-
-fig, ax = plt.subplots()
-
-ax.hist(df["price"], bins=50)
-
-ax.set_xlabel("Price (€)")
-ax.set_ylabel("Number of properties")
-
-st.pyplot(fig)
-
-
-# ---------------------------------------------------
-# PRICE VS SURFACE
+# MODEL FORMULA
 # ---------------------------------------------------
 
 st.write("---")
-st.header("Price vs Surface")
-
-fig2, ax2 = plt.subplots()
-
-ax2.scatter(df["surface"], df["price"], alpha=0.3)
 
-ax2.set_xlabel("Surface (m²)")
-ax2.set_ylabel("Price (€)")
-
-st.pyplot(fig2)
-
-
-# ---------------------------------------------------
-# MODEL EXPLANATION
-# ---------------------------------------------------
-
-st.write("---")
-st.header("Model")
-
-st.write("The prediction is based on the following linear regression model:")
+st.write("### Model Formula")
 
 st.latex(
-r'''
+r"""
 Price =
 -467418
 + 4932 \times Surface
 - 29954 \times Rooms
 + 79383 \times NeighborhoodScore
-'''
+"""
+)
+
+st.write(
+"""
+The prediction is based on a **linear regression model** trained on
+housing transactions in Île-de-France.
+"""
 )