Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,22 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import joblib
+import numpy as np
 
-"""
-# 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])),
-    ))
+with open("house_model", "rb") as f:
+    model = joblib.load(f)
+
+st.title(":blue[House] Price Analysis :house:")
+bedrooms = st.number_input("bedrooms:", min_value=1, max_value=10, step=1)
+bathrooms = st.number_input("bathrooms:", min_value=1, max_value=10, step=1)
+sqft_living = st.number_input("sqft_living:", min_value=100, max_value=10000, step=1)
+sqft_lot = st.number_input("sqft_lot:", min_value=1, max_value=10, step=1)
+floors = st.number_input("floors:", min_value=1, max_value=10, step=1)
+yr_built = st.number_input("yr_built:", min_value=1960, max_value=2025, step=1)
+yr_renovated = st.number_input("yr_renovated:", min_value=1960, max_value=2025, step=1)
+
+if st.button("Analysis"):
+    st.snow()
+    model_input = np.array([[bedrooms, bathrooms, sqft_living, sqft_lot, floors, yr_built, yr_renovated]])
+    prediction = model.predict(model_input)
+    formatted_pred = round(prediction[0],2)
+    st.write(f"You House Price is: {formatted_pred}")