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untitled3.py

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+# -*- coding: utf-8 -*-
+"""Untitled3.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1BTaF9lue6oXAqEx5zFRq1cnWWQ9YKCiQ
+"""
+
+import pandas as pd
+import numpy as np
+import torch
+from transformers import BertTokenizer
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn.feature_extraction.text import CountVectorizer
+
+
+# Load dataset
+file_path = 'spam_ham_dataset.csv'
+df = pd.read_csv(file_path)
+df.head()
+
+# Preprocessing
+#.str.replace(r'[^\w\s]', '', regex=True) removes everthing except letters, numbers, and spaces
+# df['text'].str.lower() converts everything in the text column to lower case only
+df['text'] = df['text'].str.lower().str.replace(r'[^\w\s]', '', regex=True)
+df['text'].head()
+
+
+sns.countplot(x=df['label'])
+plt.title("Spam vs Ham Distribution")
+plt.show()
+
+# Calculate text length metrics
+df['char_count'] = df['text'].apply(len)
+df['word_count'] = df['text'].apply(lambda x: len(x.split()))
+# Plot word count distribution for spam and ham
+plt.figure(figsize=(12, 5))
+sns.histplot(data=df, x='word_count', hue='label', bins=30, kde=True)
+plt.xlim(0, 1000)
+plt.title("Word Count Distribution by Label")
+plt.xlabel("Number of Words")
+plt.ylabel("Frequency")
+plt.show()
+
+def get_top_words(corpus, n=None):
+    vec = CountVectorizer(stop_words='english').fit(corpus)
+    bag_of_words = vec.transform(corpus)
+    sum_words = bag_of_words.sum(axis=0)
+    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]
+    words_freq = sorted(words_freq, key=lambda x: x[1], reverse=True)
+    return words_freq[:n]
+
+# Top 10 words for spam
+top_spam_words = get_top_words(df[df['label'] == "spam"]['text'], n=10)
+print("Top spam words:", top_spam_words)
+
+# Top 10 words for ham
+top_ham_words = get_top_words(df[df['label'] == "ham"]['text'], n=10)
+print("Top ham words:", top_ham_words)
+
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.metrics import classification_report
+
+# TF-IDF Vectorization
+vectorizer = TfidfVectorizer()
+X = vectorizer.fit_transform(df['text'])
+y = df['label_num']
+
+# Train-Test Split
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train Naïve Bayes Model
+nb_model = MultinomialNB()
+nb_model.fit(X_train, y_train)
+
+# Predictions
+y_pred = nb_model.predict(X_test)
+print(classification_report(y_test, y_pred))
+
+import pandas as pd
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from transformers import BertTokenizer, BertForSequenceClassification
+from torch.utils.data import Dataset, DataLoader
+
+# Load dataset
+file_path = 'spam_ham_dataset.csv'
+df = pd.read_csv(file_path)
+
+# Convert label column to numeric (0 for ham, 1 for spam)
+df['label_num'] = df['label'].astype('category').cat.codes
+
+# Load tokenizer
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+
+# Tokenize dataset
+encodings = tokenizer(df['text'].tolist(), padding=True, truncation=True, max_length=128, return_tensors="pt")
+labels = torch.tensor(df['label_num'].values)
+
+# Custom Dataset
+class SpamDataset(Dataset):
+    def __init__(self, encodings, labels):
+        self.encodings = encodings
+        self.labels = labels
+
+    def __len__(self):
+        return len(self.labels)
+
+    def __getitem__(self, idx):
+        item = {key: val[idx] for key, val in self.encodings.items()}  # Keep as PyTorch tensors
+        item['labels'] = torch.tensor(self.labels[idx], dtype=torch.long)  # Ensure labels are `long`
+        return item
+
+# Create dataset
+dataset = SpamDataset(encodings, labels)
+
+# Split dataset (80% train, 20% validation)
+train_size = int(0.8 * len(dataset))
+val_size = len(dataset) - train_size
+train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])
+
+# DataLoader Function (Fix Collate)
+def collate_fn(batch):
+    keys = batch[0].keys()
+    collated = {key: torch.stack([b[key] for b in batch]) for key in keys}
+    return collated
+
+# Create DataLoader
+train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True, collate_fn=collate_fn)
+val_loader = DataLoader(val_dataset, batch_size=8, shuffle=False, collate_fn=collate_fn)
+
+# Load BERT model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)
+model.to(device)
+
+# Define optimizer and loss function
+optimizer = optim.AdamW(model.parameters(), lr=5e-5)
+loss_fn = nn.CrossEntropyLoss()
+
+# Training Loop
+EPOCHS = 10
+
+for epoch in range(EPOCHS):
+    model.train()
+    total_loss = 0
+
+    for batch in train_loader:
+        optimizer.zero_grad()
+
+        # Move batch to device
+        inputs = {key: val.to(device) for key, val in batch.items()}
+        labels = inputs.pop("labels").to(device)  # Move labels to device
+
+        # Forward pass
+        outputs = model(**inputs)
+        loss = loss_fn(outputs.logits, labels)
+
+        # Backward pass
+        loss.backward()
+        optimizer.step()
+
+        total_loss += loss.item()
+
+    avg_loss = total_loss / len(train_loader)
+    print(f"Epoch {epoch+1}, Loss: {avg_loss:.4f}")
+
+print("Training complete!")
+
+from sklearn.metrics import classification_report
+from transformers import BertTokenizer
+import torch
+import torch.nn.functional as F
+
+# Classification function
+def classify_email(email_text):
+    model.eval()  # Set model to evaluation mode
+
+    with torch.no_grad():
+        # Tokenize and convert input text to tensor
+        inputs = tokenizer(email_text, padding=True, truncation=True, max_length=256, return_tensors="pt")
+
+        # Move inputs to the appropriate device
+        inputs = {key: val.to(device) for key, val in inputs.items()}
+
+        # Get model predictions
+        outputs = model(**inputs)
+        logits = outputs.logits
+
+        # Convert logits to predicted class
+        predictions = torch.argmax(logits, dim=1)
+
+        # Convert logits to probabilities using softmax
+        probs = F.softmax(logits, dim=1)
+        confidence = torch.max(probs).item() * 100  # Convert to percentage
+
+    # Convert numeric prediction to label
+    result = "Spam" if predictions.item() == 1 else "Ham"
+
+    return {
+        "result": result,
+        "confidence": f"{confidence:.2f}%",
+    }
+
+# Evaluation function with detailed classification report
+def evaluate_model_with_report(val_loader):
+    model.eval()  # Set model to evaluation mode
+    y_true = []
+    y_pred = []
+    correct = 0
+    total = 0
+
+    with torch.no_grad():
+        for batch in val_loader:
+            inputs = {key: val.to(device) for key, val in batch.items()}
+            labels = inputs.pop("labels").to(device)
+
+            outputs = model(**inputs)
+            predictions = torch.argmax(outputs.logits, dim=1)
+
+            # Collect labels and predictions
+            y_true.extend(labels.cpu().numpy())
+            y_pred.extend(predictions.cpu().numpy())
+
+            # Calculate accuracy
+            correct += (predictions == labels).sum().item()
+            total += labels.size(0)
+
+    # Calculate accuracy
+    accuracy = correct / total if total > 0 else 0
+    print(f"Validation Accuracy: {accuracy:.4f}")
+
+    # Print classification report
+    print("\nClassification Report:")
+    print(classification_report(y_true, y_pred, target_names=["Ham", "Spam"]))
+
+    return accuracy
+
+# Run evaluation with classification report
+accuracy = evaluate_model_with_report(val_loader)
+print(f"Model Validation Accuracy: {accuracy:.4f}")
+
+## App Deployment Functions
+
+def generate_performance_metrics():
+    y_pred = model.predict(X_test)
+    accuracy = evaluate_model_with_report(val_loader)
+    report = classification_report(y_true, y_pred, target_names=["Ham", "Spam"])
+    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%}"
+    }
+
+def email_analysis_pipeline(email_text):
+    results = classify_email(email_text)
+    accuracy = evaluate_model_with_report(val_loader)
+    return {
+        results["result"],
+        results["confidence"],
+        accuracy
+    }
+
+## Gradio Interface
+
+!pip install gradio
+import gradio as gr
+
+# Create Gradio Interface
+def create_interface():
+    performance_metrics = generate_performance_metrics()
+
+    # Introduction - Title + Brief Description
+    with gr.Blocks(css=custom_css) as interface:
+        gr.Markdown("Spam Email Classification")
+        gr.Markdown(
+            """
+            Brief description of the project here
+
+            """
+        )
+
+        # Email Text Input
+        with gr.Row():
+            email_input = gr.Textbox(
+                lines=8, placeholder="Type or paste your email content here...", label="Email Content"
+            )
+
+        # Email Text Results and Analysis
+        with gr.Row():
+            result_output = gr.HTML(label="Classification Result") # label = [function that prints classification result]
+            confidence_output = gr.Textbox(label="Confidence Score", interactive=False)
+            accuracy_output = gr.Textbox(label="Accuracy", interactive=False)
+
+
+        analyze_button = gr.Button("Analyze Email 🕵️‍♂️")
+
+        analyze_button.click(
+            fn=email_analysis_pipeline,
+            inputs=email_input,
+            outputs=[result_output, confidence_output, accuracy_output]
+        )
+
+        # Analysis
+        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:** The 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.
+            """
+        )
+
+    return interface
+
+# Launch the interface
+interface = create_interface()
+interface.launch(share=True)
+
+## CSS
+
+# 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);
+}
+"""
+
+## Original
+
+from sklearn.metrics import classification_report
+
+# Collect predictions and true labels
+y_true = []
+y_pred = []
+
+model.eval()
+with torch.no_grad():
+    for batch in val_loader:
+        inputs = {key: val.to(device) for key, val in batch.items()}
+        labels = inputs.pop("labels").to(device)
+
+        outputs = model(**inputs)
+        predictions = torch.argmax(outputs.logits, dim=1)
+
+        y_true.extend(labels.cpu().numpy())
+        y_pred.extend(predictions.cpu().numpy())
+
+# Print detailed classification report
+print(classification_report(y_true, y_pred, target_names=["Ham", "Spam"]))