Update README.md

README.md

@@ -52,6 +52,68 @@ The model uses `bert-base-uncased` as the pre-trained backbone and is fine-tuned
 - **Loss:** Cross-Entropy
 
 ---
+## Gradio Build
+
+```python
+import gradio as gr
+import torch
+from transformers import BertTokenizer, BertForSequenceClassification
+
+# Load the pre-trained BERT model and tokenizer
+MODEL_PATH = "prithivMLmods/Spam-Bert-Uncased"
+tokenizer = BertTokenizer.from_pretrained(MODEL_PATH)
+model = BertForSequenceClassification.from_pretrained(MODEL_PATH)
+
+# Function to predict if a given text is Spam or Ham
+def predict_spam(text):
+    # Tokenize the input text
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    
+    # Perform inference
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        prediction = torch.argmax(logits, axis=-1).item()
+    
+    # Map prediction to label
+    if prediction == 1:
+        return "Spam"
+    else:
+        return "Ham"
+
+
+# Gradio UI - Input and Output components
+inputs = gr.Textbox(label="Enter Text", placeholder="Type a message to check if it's Spam or Ham...")
+outputs = gr.Label(label="Prediction")
+
+# List of example inputs
+examples = [
+    ["Win $1000 gift cards now by clicking here!"],
+    ["You have been selected for a lottery."],
+    ["Hello, how was your day?"],
+    ["Earn money without any effort. Click here."],
+    ["Meeting tomorrow at 10 AM. Don't be late."],
+    ["Claim your free prize now!"],
+    ["Are we still on for dinner tonight?"],
+    ["Exclusive offer just for you, act now!"],
+    ["Let's catch up over coffee soon."],
+    ["Congratulations, you've won a new car!"]
+]
+
+# Create the Gradio interface
+gr_interface = gr.Interface(
+    fn=predict_spam,
+    inputs=inputs,
+    outputs=outputs,
+    examples=examples,
+    title="Spam Detection with BERT",
+    description="Type a message in the text box to check if it's Spam or Ham using a pre-trained BERT model."
+)
+
+# Launch the application
+gr_interface.launch()
+
+```
 
 ## **🚀 How to Train the Model**