made deployment ready code

app.py

@@ -1,72 +1,72 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-import joblib
-import matplotlib.pyplot as plt
-
-# Load the model and columns
-lr_clf = joblib.load("C:/Users/vijay/OneDrive/Desktop/Banglore Housing Project/banglore_home_prices_model.pkl")
-X_columns = pd.read_csv("C:/Users/vijay/OneDrive/Desktop/Banglore Housing Project/dora.csv")
-OHE = pd.read_csv("C:/Users/vijay/OneDrive/Desktop/Banglore Housing Project/B5.csv")
-locations = OHE['location'].tolist()
-
-# Non-changeable variables
-bhk1 = 5
-bath1 = 5
-
-def predict_price(location, sqft, bath, bhk):
-    loc_index = np.where(X_columns.columns == location)[0][0]
-
-    x = np.zeros(len(X_columns.columns))
-    x[0] = sqft
-    x[1] = bath
-    x[2] = bhk
-    if loc_index >= 0:
-        x[loc_index] = 1
-
-    return lr_clf.predict([x])[0]
-
-def get_price_predictions(location, sqft, bhk):
-    all_predictions = []
-    for bhk_val in range(1, bhk+1):
-        predictions = []
-        for bath in range(1, 6):
-            price_prediction = predict_price(location, sqft, bath, bhk_val)
-            predictions.append(price_prediction)
-        all_predictions.append(predictions)
-    return all_predictions
-
-st.title('House Price Prediction')
-
-# Sidebar with area and location selection
-sqft = st.sidebar.slider('Select the area in sq meters:', min_value=500.0, max_value=3000.0, value=500.0)
-location = st.sidebar.selectbox('Select a location:', locations)
-bhk = st.sidebar.slider('Select BHK (1-5):', min_value=1, max_value=5)
-bath = st.sidebar.slider('Select Bathrooms (1-5):', min_value=1, max_value=5)
-
-estimated_price = predict_price(location, sqft, bath, bhk)
-st.write(f"Estimated Price per sqft : ₹ {estimated_price}")
-
-# Predict prices for different numbers of BHKs
-predictions = get_price_predictions(location, sqft, bhk1)
-
-# Display a spreadsheet-like table of prices
-prices_table = pd.DataFrame(predictions, columns=[f"{i+1} BHK" for i in range(bhk1)], index=[f"{i} Bathrooms" for i in range(1, bath1+1)])
-st.table(prices_table)
-
-# Plot graphs for each number of BHKs
-fig, axs = plt.subplots(bhk1, 1, figsize=(10, bhk1*5), sharex=True)
-bath_values = range(1, 6)
-colors = ['blue', 'green', 'red', 'purple', 'orange']  # Define different colors for each BHK
-
-for i in range(bhk1):
-    axs[i].plot(bath_values, predictions[i], label=f'{i+1} BHK', color=colors[i])  # Use a different color for each BHK
-    axs[i].set_ylabel('Predicted Price per sqft (in ₹)')
-    axs[i].set_title(f'Predicted Price for {i+1} BHK (in ₹)')
-    axs[i].legend(loc='center left', bbox_to_anchor=(1, 0.5))  # Position legend to the right of the graph
-
-# Set common x-axis label
-fig.text(0.5, 0.04, 'Number of Bathrooms', ha='center', va='center')
-
-plt.tight_layout(pad=3.0)
-st.pyplot(fig)
+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+import matplotlib.pyplot as plt
+
+# Load the model and columns
+lr_clf = joblib.load("banglore_home_prices_model.pkl")
+X_columns = pd.read_csv("dora.csv")
+OHE = pd.read_csv("B5.csv")
+locations = OHE['location'].tolist()
+
+# Non-changeable variables
+bhk1 = 5
+bath1 = 5
+
+def predict_price(location, sqft, bath, bhk):
+    loc_index = np.where(X_columns.columns == location)[0][0]
+
+    x = np.zeros(len(X_columns.columns))
+    x[0] = sqft
+    x[1] = bath
+    x[2] = bhk
+    if loc_index >= 0:
+        x[loc_index] = 1
+
+    return lr_clf.predict([x])[0]
+
+def get_price_predictions(location, sqft, bhk):
+    all_predictions = []
+    for bhk_val in range(1, bhk+1):
+        predictions = []
+        for bath in range(1, 6):
+            price_prediction = predict_price(location, sqft, bath, bhk_val)
+            predictions.append(price_prediction)
+        all_predictions.append(predictions)
+    return all_predictions
+
+st.title('House Price Prediction')
+
+# Sidebar with area and location selection
+sqft = st.sidebar.slider('Select the area in sq meters:', min_value=500.0, max_value=3000.0, value=500.0)
+location = st.sidebar.selectbox('Select a location:', locations)
+bhk = st.sidebar.slider('Select BHK (1-5):', min_value=1, max_value=5)
+bath = st.sidebar.slider('Select Bathrooms (1-5):', min_value=1, max_value=5)
+
+estimated_price = predict_price(location, sqft, bath, bhk)
+st.write(f"Estimated Price per sqft : ₹ {estimated_price}")
+
+# Predict prices for different numbers of BHKs
+predictions = get_price_predictions(location, sqft, bhk1)
+
+# Display a spreadsheet-like table of prices
+prices_table = pd.DataFrame(predictions, columns=[f"{i+1} BHK" for i in range(bhk1)], index=[f"{i} Bathrooms" for i in range(1, bath1+1)])
+st.table(prices_table)
+
+# Plot graphs for each number of BHKs
+fig, axs = plt.subplots(bhk1, 1, figsize=(10, bhk1*5), sharex=True)
+bath_values = range(1, 6)
+colors = ['blue', 'green', 'red', 'purple', 'orange']  # Define different colors for each BHK
+
+for i in range(bhk1):
+    axs[i].plot(bath_values, predictions[i], label=f'{i+1} BHK', color=colors[i])  # Use a different color for each BHK
+    axs[i].set_ylabel('Predicted Price per sqft (in ₹)')
+    axs[i].set_title(f'Predicted Price for {i+1} BHK (in ₹)')
+    axs[i].legend(loc='center left', bbox_to_anchor=(1, 0.5))  # Position legend to the right of the graph
+
+# Set common x-axis label
+fig.text(0.5, 0.04, 'Number of Bathrooms', ha='center', va='center')
+
+plt.tight_layout(pad=3.0)
+st.pyplot(fig)