Upload app.py

app.py

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+ 
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.metrics import accuracy_score, classification_report, ConfusionMatrixDisplay
+import joblib
+import matplotlib.pyplot as plt
+from io import BytesIO
+import base64
+import gradio as gr
+import re
+
+# Load and preprocess dataset
+dataset = pd.read_csv('/content/email_spam (1).csv', on_bad_lines='skip', engine='python')
+
+# Drop rows where 'spam' or 'text' is NaN and convert 'spam' to numeric
+dataset.dropna(subset=['spam', 'text'], inplace=True)
+dataset['spam'] = pd.to_numeric(dataset['spam'], errors='coerce')
+
+# Remove any rows where 'spam' is NaN after conversion and convert 'spam' to integers
+dataset.dropna(subset=['spam'], inplace=True)
+dataset['spam'] = dataset['spam'].astype(int)
+
+# Vectorize the text data
+vectorizer = CountVectorizer()
+X = vectorizer.fit_transform(dataset['text'])
+y = dataset['spam']
+
+# 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)
+
+# Train the Naive Bayes model
+model = MultinomialNB()
+model.fit(X_train, y_train)
+
+# Save the model and vectorizer
+joblib.dump(model, 'spam_model.pkl')
+joblib.dump(vectorizer, 'spam_vectorizer.pkl')
+
+# Reload for consistency
+model = joblib.load('spam_model.pkl')
+vectorizer = joblib.load('spam_vectorizer.pkl')
+
+# List of spammy keywords
+spam_keywords = [
+    "win", "free", "urgent", "money", "credit", "loan", "offer", "buy now",
+    "limited time", "click here", "guaranteed", "congratulations", "winner"
+]
+
+# Helper function to highlight spammy keywords
+def highlight_keywords(text):
+    highlighted = text
+    for keyword in spam_keywords:
+        pattern = re.compile(rf"{keyword}", re.IGNORECASE)
+        highlighted = pattern.sub(f"<span class='highlight'>{keyword}</span>", highlighted)
+    return highlighted
+
+# Prediction function
+def classify_email(email_text):
+    email_vector = vectorizer.transform([email_text])
+    prediction = model.predict(email_vector)
+    confidence = model.predict_proba(email_vector).max() * 100
+    result = "Spam" if prediction[0] == 1 else "Ham"
+
+    highlighted_text = highlight_keywords(email_text)
+    color = "red" if result == "Spam" else "green"
+    emoji = "📧" if result == "Ham" else "⚠️"
+    advice = "<b>Be careful!</b> This might be a scam." if result == "Spam" else "<b>This email seems safe.</b>"
+
+    return {
+        "result": f"<span style='color: {color}; font-size: 1.5em;'>{emoji} {result}</span>",
+        "confidence": f"{confidence:.2f}%",
+        "highlighted": highlighted_text,
+        "spammy_keywords": ", ".join(
+            [kw for kw in spam_keywords if kw.lower() in email_text.lower()]
+        ),
+        "advice": advice
+    }
+
+# Generate performance metrics
+def generate_performance_metrics():
+    y_pred = model.predict(X_test)
+    accuracy = accuracy_score(y_test, y_pred)
+    report = classification_report(y_test, y_pred, output_dict=True)
+
+    # Confusion matrix plot
+    fig, ax = plt.subplots(figsize=(6, 6))
+    ConfusionMatrixDisplay.from_predictions(y_test, y_pred, ax=ax, cmap='Blues')
+    plt.title("Confusion Matrix")
+    plt.tight_layout()
+
+    # Save plot as a base64 string
+    buf = BytesIO()
+    plt.savefig(buf, format="png")
+    buf.seek(0)
+    img_base64 = base64.b64encode(buf.getvalue()).decode("utf-8")
+    buf.close()
+
+    return {
+        "accuracy": f"{accuracy:.2%}",
+        "precision": f"{report['1']['precision']:.2%}",
+        "recall": f"{report['1']['recall']:.2%}",
+        "f1_score": f"{report['1']['f1-score']:.2%}",
+        "confusion_matrix_plot": img_base64,
+    }
+
+# Updated CSS
+custom_css = """
+body {
+    font-family: 'Arial', sans-serif;
+    background-image: url('https://cdn.pixabay.com/photo/2016/11/19/15/26/email-1839873_1280.jpg');
+    background-size: cover;
+    background-position: center;
+    background-attachment: fixed;
+    color: #333;
+}
+
+h1, h2, h3 {
+    text-align: center;
+    color: #ffffff;
+    text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.7);
+}
+
+.gradio-container {
+    background-color: rgba(255, 255, 255, 0.8);
+    border-radius: 10px;
+    padding: 20px;
+    box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.3);
+}
+
+button {
+    background-color: #1e90ff;
+    color: white;
+    padding: 10px 20px;
+    border: none;
+    border-radius: 5px;
+    cursor: pointer;
+    font-size: 1.2em;
+    transition: transform 0.2s, background-color 0.3s;
+}
+
+button:hover {
+    background-color: #1c86ee;
+    transform: scale(1.05);
+}
+
+.highlight {
+    background-color: #ffeb3b;
+    font-weight: bold;
+    padding: 0 3px;
+    border-radius: 3px;
+}
+
+.metric {
+    font-size: 1.2em;
+    text-align: center;
+    color: #ffffff;
+    background-color: #4CAF50;
+    border-radius: 8px;
+    padding: 10px;
+    margin: 10px 0;
+    box-shadow: 2px 2px 5px rgba(0, 0, 0, 0.2);
+}
+"""
+
+# Create Gradio Interface
+def create_interface():
+    performance_metrics = generate_performance_metrics()
+
+    with gr.Blocks(css=custom_css) as interface:
+        gr.Markdown("# 📩 Advanced Email Spam Classifier")
+        gr.Markdown(
+            """
+            ### Enter the content of an email below to classify it as Spam or Ham.
+            The tool uses **machine learning** to analyze email content, highlights spammy keywords, and shows key performance analytics.
+            """
+        )
+
+        with gr.Row():
+            with gr.Column():
+                email_input = gr.Textbox(
+                    lines=8, placeholder="Type or paste your email content here...", label="Email Content"
+                )
+            with gr.Column():
+                result_output = gr.HTML(label="Classification Result")
+                confidence_output = gr.Textbox(label="Confidence Score", interactive=False)
+                highlighted_output = gr.HTML(label="Highlighted Text")
+                keywords_output = gr.Textbox(label="Spam Keywords Detected", interactive=False)
+                advice_output = gr.HTML(label="Advice")
+
+        analyze_button = gr.Button("Analyze Email 🕵️‍♂️")
+
+        def email_analysis_pipeline(email_text):
+            results = classify_email(email_text)
+            return (
+                results["result"],
+                results["confidence"],
+                results["highlighted"],
+                results["spammy_keywords"],
+                results["advice"]
+            )
+
+        analyze_button.click(
+            fn=email_analysis_pipeline,
+            inputs=email_input,
+            outputs=[
+                result_output,
+                confidence_output,
+                highlighted_output,
+                keywords_output,
+                advice_output
+            ]
+        )
+
+        gr.Markdown("## 📊 Model Performance Analytics")
+        with gr.Row():
+            with gr.Column():
+                gr.Textbox(value=performance_metrics["accuracy"], label="Accuracy", interactive=False, elem_classes=["metric"])
+                gr.Textbox(value=performance_metrics["precision"], label="Precision", interactive=False, elem_classes=["metric"])
+                gr.Textbox(value=performance_metrics["recall"], label="Recall", interactive=False, elem_classes=["metric"])
+                gr.Textbox(value=performance_metrics["f1_score"], label="F1 Score", interactive=False, elem_classes=["metric"])
+            with gr.Column():
+                gr.Markdown("### Confusion Matrix")
+                gr.HTML(f"<img src='data:image/png;base64,{performance_metrics['confusion_matrix_plot']}' style='max-width: 100%; height: auto;' />")
+
+        gr.Markdown("## 📘 Glossary and Explanation of Labels")
+        gr.Markdown(
+            """
+            ### Labels:
+            - **Spam:** Unwanted or harmful emails flagged by the system.
+            - **Ham:** Legitimate, safe emails.
+
+            ### Metrics:
+            - **Accuracy:** Percentage of correct classifications.
+            - **Precision:** Out of predicted Spam, how many are actually Spam.
+            - **Recall:** Out of all actual Spam emails, how many are predicted as Spam.
+            - **F1 Score:** Harmonic mean of Precision and Recall.
+
+            ### Confusion Matrix:
+            Shows the distribution of true vs predicted labels.
+            """
+        )
+
+    return interface
+
+# Launch the interface
+interface = create_interface()
+interface.launch(share=True)