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spam_app.py

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+import gradio as gr
+import joblib
+import xgboost as xgb
+import numpy as np
+
+def classify_email(email_text):
+    tfidf = joblib.load('tfidf_vectorizer.joblib')
+    model = joblib.load('spam_model.joblib')
+    email_tfidf = tfidf.transform([email_text])
+    email_dmatrix = xgb.DMatrix(email_tfidf)
+    prediction = model.predict(email_dmatrix)[0]
+    confidence = max(prediction, 1 - prediction)
+    label = "Spam" if prediction > 0.5 else "Not Spam"
+    return {label: float(confidence)}
+
+def analyze_email(email_text):
+    tfidf = joblib.load('tfidf_vectorizer.joblib')
+    model = joblib.load('spam_model.joblib')
+    email_tfidf = tfidf.transform([email_text])
+    email_dmatrix = xgb.DMatrix(email_tfidf)
+    prediction = model.predict(email_dmatrix)[0]
+    confidence = max(prediction, 1 - prediction)
+    label = "Spam" if prediction > 0.5 else "Not Spam"
+    
+    # Get feature importance
+    feature_names = tfidf.get_feature_names_out()
+    feature_importance = model.get_score(importance_type='gain')
+    top_features = sorted(feature_importance.items(), key=lambda x: x[1], reverse=True)[:5]
+    
+    analysis = f"Classification: {label} (Confidence: {confidence:.2%})\n\n"
+    analysis += "Top 5 influential words:\n"
+    for feature, importance in top_features:
+        if feature in email_text.lower():
+            analysis += f"- {feature}: {importance:.2f}\n"
+    
+    return analysis
+
+# Create Gradio interface
+with gr.Blocks(css="footer {visibility: hidden}") as iface:
+    gr.Markdown(
+    """
+    # 🚀  Spam Email Classifier
+    
+    Using Machine Learning to detect spam emails with high accuracy!
+    """
+    )
+    with gr.Row():
+        with gr.Column(scale=2):
+            email_input = gr.Textbox(lines=5, label="Enter email text")
+            with gr.Row():
+                classify_btn = gr.Button("Classify")
+                analyze_btn = gr.Button("Detailed Analysis")
+        with gr.Column(scale=1):
+            label_output = gr.Label(label="Classification")
+            analysis_output = gr.Textbox(label="Detailed Analysis", lines=8)
+    
+    examples = [
+        ["Get fat quick! Buy our cheese burger now!"],
+        ["Hi Ajibola, let's go out on a date tonight"],
+        ["Congratulations! You've won a free iPhone. Click here to claim."],
+        ["Please find attached the report for Q2 sales figures."]
+    ]
+    gr.Examples(examples, inputs=email_input)
+    
+    classify_btn.click(classify_email, inputs=email_input, outputs=label_output)
+    analyze_btn.click(analyze_email, inputs=email_input, outputs=analysis_output)
+    
+    gr.Markdown(
+    """
+    ### How it works
+    This classifier uses an XGBoost model trained on a large dataset of over 190,000 emails.
+    The model achieved a 98% accuracy on the training data and 94% accuracy on the test data.
+    It analyzes the content and structure of the email to determine if it's spam or not.
+    
+    ### Tips for use
+    - Enter the full text of the email for best results
+    - The 'Detailed Analysis' shows the top words influencing the classification
+    - Confidence score indicates how sure the model is about its prediction
+    """
+    )
+
+# Launch the interface
+iface.launch()

spam_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8a0c80f4df9fd5df5d070bcaf91424cdfd885d8270e5d2bc417753df1a0ab4b9
+size 295684

spam_requirements.txt

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+gradio
+joblib
+xgboost
+scikit-learn
+numpy

tfidf_vectorizer.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0176aadd7ba5b4b3c4841a6dfabfcc04b85e17ed59325905d6bfbf02e6ec92e8
+size 2326758