Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import pickle
+from sklearn.preprocessing import LabelEncoder
+
+# Load the trained model from data.pkl
+def load_model():
+  with open('data.pkl', 'rb') as file:
+    model = pickle.load(file)
+  return model
+
+# Define the prediction function using the loaded model
+def predict_user_profile(inputs):
+  # Preprocess the input data
+  lang_encoder = LabelEncoder()
+  lang_code = lang_encoder.fit_transform([inputs['Language']])[0]
+
+  # Create a DataFrame from the user input dictionary
+  df = pd.DataFrame.from_dict([inputs])
+
+  # Select the relevant feature columns used during model training
+  feature_columns_to_use = ['statuses_count', 'followers_count', 'friends_count',
+                           'favourites_count', 'listed_count', 'lang_code']
+  df_features = df[feature_columns_to_use]
+
+  # Load the pre-trained model
+  model = load_model()
+
+  # Make predictions using the loaded model
+  prediction = model.predict(df_features)
+
+  # Return the predicted class label (0 for fake, 1 for genuine)
+  return "Genuine" if prediction[0] == 1 else "Fake"
+
+# Define the Gradio interface
+inputs = [
+  gr.Textbox(label="statuses_count"),
+  gr.Textbox(label="followers_count"),
+  gr.Textbox(label="friends_count"),
+  gr.Textbox(label="favourites_count"),
+  gr.Textbox(label="listed_count"),
+  gr.Textbox(label="name"),
+  gr.Textbox(label="Language"),
+]
+
+outputs = gr.Textbox(label="Prediction")
+
+# Create the Gradio interface
+interface = gr.Interface(fn=predict_user_profile, inputs=inputs, outputs=outputs,
+                         title='User Profile Classifier',
+                         description='Predict whether a user profile is genuine or fake.')
+