Create app1.py

app1.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+from sklearn.datasets import fetch_california_housing
+import pickle
+
+# Load the data
+california = fetch_california_housing()
+df = pd.DataFrame(california.data, columns=california.feature_names)
+df['MedHouseVal'] = california.target
+
+# Prepare the data for the model
+X = df[['MedInc']]
+y = df['MedHouseVal']
+
+# Split the data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train the model
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+# Save the model
+with open("linear_regression_model.pkl", "wb") as file:
+    pickle.dump(model, file)
+
+# Load the model
+with open("linear_regression_model.pkl", "rb") as file:
+    model = pickle.load(file)
+
+# Define prediction function
+def predict(med_inc):
+    X_new = np.array([[med_inc]])
+    prediction = model.predict(X_new)
+    return prediction[0]
+
+# Create Gradio interface
+iface = gr.Interface(fn=predict, inputs="number", outputs="number", title="California Housing Price Prediction")
+iface.launch()