Create app.py

app.py

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+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+import joblib
+import gradio as gr
+
+# Load the data from the CSV file
+data = pd.read_csv('data.csv')
+
+# Encode 'Price' column into numerical values
+data['Price'] = data['Price'].apply(lambda x: 0 if x == 'Free' else 1)
+
+# Convert 'Size' and 'Reviews' columns to numerical values
+data['Size'] = data['Size'].str.replace('MB', '').astype(float)
+data['Reviews'] = data['Reviews'].str.replace('M', '').astype(float)
+
+# Select the features (reviews, size, and price) and the target variable (rating)
+X = data[['Reviews', 'Size', 'Price']]
+y = data['Rating']
+
+# 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)
+
+# Create a linear regression model
+model = LinearRegression()
+
+# Train the model
+model.fit(X_train, y_train)
+
+# Save the trained model
+joblib.dump(model, 'linear_regression_model.pkl')
+
+# Define a function to make predictions using the model
+def predict_rating(reviews, size, price):
+    # Load the trained model
+    loaded_model = joblib.load('linear_regression_model.pkl')
+    # Make predictions using the loaded model
+    predicted_rating = loaded_model.predict([[reviews, size, price]])
+    return predicted_rating[0]
+
+# Create a Gradio interface for the model
+iface = gr.Interface(fn=predict_rating, inputs=["number", "number", "number"], outputs="number", title="App Rating Predictor", examples=[[20, 25.1, 0], [45, 26.7, 1], [60, 30.2, 0]], description="Enter the number of reviews, size(without 'MB' word), and price(0 = paid, 1 = free) of your app to predict its rating.")
+
+# Launch the Gradio interface with a user guide
+iface.launch(share=False, debug=True, enable_queue=True)