Delete app.py

app.py

@@ -1,41 +0,0 @@
-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()