Update app.py

app.py

@@ -1,6 +1,75 @@
-import streamlit 
-
 import streamlit as st
+import numpy as np
+import pickle
+import pandas as pd
+
+# Load Model
+try:
+    with open("final_model.pkl", "rb") as f:
+        model = pickle.load(f)
+    st.success("✅ Model loaded successfully!")
+except FileNotFoundError:
+    st.error("❌ Model file not found! Please upload `final_model.pkl`.")
+    model = None
+
+# Apply Dark Theme CSS
+st.markdown(
+    """
+    <style>
+        body {
+            background-color: #121212;
+            color: #FFFFFF;
+        }
+        .stApp {
+            background-color: #121212;
+            color: #FFFFFF;
+        }
+        .title {
+            text-align: center;
+            font-size: 36px;
+            font-weight: bold;
+            color: #BB86FC;
+        }
+        .stButton > button {
+            width: 100%;
+            background-color: #6200EE;
+            color: white;
+            font-size: 18px;
+            border-radius: 8px;
+            padding: 10px;
+            transition: 0.3s ease-in-out;
+        }
+        .stButton > button:hover {
+            background-color: #3700B3;
+            transform: scale(1.05);
+        }
+        .result-box {
+            text-align: center;
+            font-size: 20px;
+            font-weight: bold;
+            color: white;
+            padding: 12px;
+            border-radius: 8px;
+            margin-top: 20px;
+            box-shadow: 0px 4px 8px rgba(255, 255, 255, 0.2);
+            transition: 0.3s ease-in-out;
+        }
+        .result-box:hover {
+            transform: scale(1.05);
+        }
+        .high-risk {
+            background-color: #D32F2F;
+        }
+        .low-risk {
+            background-color: #388E3C;
+        }
+        .probability {
+            background-color: #FFA726;
+        }
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
 
 # Title of the application
 st.markdown("<h1 class='title'>🏡 House Price Predictor</h1>", unsafe_allow_html=True)
@@ -17,3 +86,22 @@ with st.expander("🔹 **Property Details**", expanded=True):
     RESALE = st.selectbox("RESALE", [1, 0])
     LONGITUDE = st.number_input("LONGITUDE", min_value=-37.713008, max_value=39.573320499999994, value=20.750000)
     LATITUDE = st.number_input("LATITUDE", min_value=-121.761248, max_value=152.962676, value=77.324137)
+
+if st.button("🔍 Predict Price"):
+    try:
+        # Create input data for prediction
+        input_data = [[POSTED_BY, UNDER_CONSTRUCTION, RERA, bhk_no, BHK_OR_RK, SQUARE_FT,
+                       READY_TO_MOVE, RESALE, LONGITUDE, LATITUDE]]
+
+        # Make prediction using the model
+        predicted_price = model.predict(input_data)[0]
+
+        # Display predicted price
+        st.markdown(
+            f"<div class='result-box'>🏠 Predicted Price: ₹ {predicted_price:.2f} Lakhs</div>",
+            unsafe_allow_html=True,
+        )
+
+    except Exception as e:
+        st.error(f"⚠️ Prediction failed: {e}")
+