Create app.py

app.py

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+import pandas as pd
+import gradio as gr
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.naive_bayes import MultinomialNB
+
+# Load and clean the dataset
+data = pd.read_csv(r"spam.csv")
+data.drop_duplicates(inplace=True)
+data['Category'] = data['Category'].replace(['ham', 'spam'], ['Not spam', 'spam'])
+
+# Prepare data
+X = data['Message']
+y = data['Category']
+
+# Split into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+# Convert text data to numerical features
+vectorizer = CountVectorizer(stop_words='english')
+X_train_features = vectorizer.fit_transform(X_train)
+X_test_features = vectorizer.transform(X_test)
+
+# Train the model
+model = MultinomialNB()
+model.fit(X_train_features, y_train)
+
+# Define prediction function
+def predict_spam(message):
+    message_features = vectorizer.transform([message])
+    prediction = model.predict(message_features)[0]
+    return f"Prediction: {prediction}"
+
+# Launch Gradio interface
+gr.Interface(
+    fn=predict_spam,
+    inputs="text",
+    outputs="text",
+    title="📩 Spam Detection with Gradio",
+    description="Enter a message and the model will predict whether it's spam or not."
+).launch()