Create app.py

app.py

@@ -0,0 +1,61 @@
+# prompt: make a web app for house price using Deep Learning ang and dashbording using Gradio
+#!pip install scikit-learn --upgrade
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.neural_network import MLPRegressor
+from sklearn.metrics import mean_squared_error
+import gradio as gr
+
+# ... (Rest of your code)
+# Load the dataset
+df = pd.read_csv('/content/sample_data/california_housing_train.csv')
+
+# Select features and target
+features = df[['longitude', 'latitude', 'housing_median_age', 'total_rooms',
+       'total_bedrooms', 'population', 'households', 'median_income']]
+target = df['median_house_value']
+
+# Split the data
+X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
+
+# Standardize the data
+scaler = StandardScaler()
+X_train_scaled = scaler.fit_transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+# Train the model
+model = MLPRegressor(hidden_layer_sizes=(100,), activation='relu', solver='adam', max_iter=1000)
+model.fit(X_train_scaled, y_train)
+
+# Evaluate the model
+predictions = model.predict(X_test_scaled)
+mse = mean_squared_error(y_test, predictions)
+print(f'Mean Squared Error: {mse}')
+
+# Create Gradio interface
+def predict_house_price(longitude, latitude, housing_median_age, total_rooms,
+total_bedrooms, population, households, median_income):
+  input_data = scaler.transform([[longitude, latitude, housing_median_age, total_rooms,
+total_bedrooms, population, households, median_income]])
+  prediction = model.predict(input_data)[0]
+  return prediction
+
+iface = gr.Interface(
+  fn=predict_house_price,
+  inputs=[
+    gr.Number(label="Longitude"),  # Use gr.Number directly
+    gr.Number(label="Latitude"),
+    gr.Number(label="Housing Median Age"),
+    gr.Number(label="Total Rooms"),
+    gr.Number(label="Total Bedrooms"),
+    gr.Number(label="Population"),
+    gr.Number(label="Households"),
+    gr.Number(label="Median Income"),
+  ],
+  outputs="text",
+  title="House Price Prediction",
+  description="Enter the features to get the predicted house price."
+)
+
+iface.launch()