Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import BertTokenizer, BertForSequenceClassification
+
+MODEL_PATH = "AntiSpamInstitute/spam-detector-bert-MoE-v2.2"
+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!"],
+    ["Let's catch up over coffee soon."]
+
+]
+
+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."
+)
+
+gr_interface.launch()