Spam classifier

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load the model
+classifier = pipeline("text-classification", model="IreNkweke/HamOrSpamModel")
+
+# Define the function that will be used in the interface
+def classify_text(text):
+    result = classifier(text)
+    label = result[0]['label']
+    score = result[0]['score']
+    
+    # Assuming the model outputs scores for both classes
+    if label == 'LABEL_1':
+        spam_percentage = round(score * 100, 2)
+        not_spam_percentage = round((1 - score) * 100, 2)
+    else:
+        spam_percentage = round((1 - score) * 100, 2)
+        not_spam_percentage = round(score * 100, 2)
+    
+    label_mapping = {
+        'LABEL_0': 'Ham',  # Non-spam
+        'LABEL_1': 'Spam'  # Spam
+    }
+    
+    return label_mapping[label], spam_percentage, not_spam_percentage
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=classify_text,
+    inputs=gr.Textbox(
+        label="Input Text",
+        placeholder="Enter your message here...",
+        lines=4
+    ),
+    outputs=[
+        gr.Textbox(label="Class", placeholder="Classification result", lines=1),
+        gr.Number(label="Spam (%)"),
+        gr.Number(label="Not Spam (%)")
+    ],
+    title="Spam Classifier",
+    description="Classify messages as Spam or Ham with percentage confidence. Enter a message below to see the classification and confidence percentages.",
+    examples=[
+        ["Congratulations! You've won a free gift card!"],
+        ["Hi! I wanted to check in and see how you’ve been doing."],
+        ["Urgent: Your account has been compromised. Please provide your login details to verify your identity."],
+        ["The meeting is scheduled for 10 AM tomorrow. Please let me know if you need any changes."],
+        ["Limited Time Offer: Buy one, get one free on all items! Shop now and save big!"]
+    ],
+    theme="default"
+)
+
+# Launch the interface
+iface.launch()

requirements.txt

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+gradio
+transformers