Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import OneHotEncoder
+ 
+def trend_analysis(df, target_column):
+    # Handle categorical variables
+    categorical_columns = ['country', 'public/pro', 'language']
+    df = pd.get_dummies(df, columns=categorical_columns)
+ 
+    # Split the data into training and testing sets
+    X_train, X_test, y_train, y_test = train_test_split(df.drop(target_column, axis=1), df[target_column], test_size=0.2, random_state=42)
+ 
+    # Train the model
+    model = LinearRegression()
+    model.fit(X_train, y_train)
+ 
+    # Use the model to make predictions
+    df['prediction'] = model.predict(df.drop(target_column, axis=1))
+ 
+    return df
+ 
+def process_csv(file_path):
+    try:
+        # Read CSV file
+        df = pd.read_csv(file_path, error_bad_lines=False)
+ 
+        # Perform trend analysis
+        target_column = 'unit price'  # Replace with the name of your target column
+        df = trend_analysis(df, target_column)
+ 
+        # Save the processed DataFrame to a new CSV file (optional)
+        df.to_csv("processed_data.csv", index=False)
+ 
+        return "Processing completed. Check 'processed_data.csv' for results."
+    except Exception as e:
+        return f"Error: {str(e)}"
+ 
+iface = gr.Interface(
+    fn=process_csv,
+    inputs=gr.File(),
+    outputs=gr.Textbox(),
+    live=True,
+)
+ 
+if __name__ == '__main__':
+    iface.launch()