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app.py

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+# -*- coding: utf-8 -*-
+"""app.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1CbDOX8PDJB6ZyLZiLMXbPyr6k7dvrs20
+"""
+
+!pip install gradio
+
+import gradio as gr
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Load the model and tokenizer
+model_name = "qarib/bert-base-qarib"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)
+
+# Preprocessing function
+def light_preprocess(text):
+    text = text.replace("@USER", "").replace("RT", "").strip()
+    return text
+
+# Prediction function
+def predict_offensive(text):
+    preprocessed_text = light_preprocess(text)
+    inputs = tokenizer(preprocessed_text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class = torch.argmax(logits, dim=1).item()
+    return "Offensive" if predicted_class == 1 else "Not Offensive"
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_offensive,
+    inputs=gr.Textbox(lines=2, placeholder="Enter text here..."),
+    outputs="text",
+    title="Offensive Language Detection",
+    description="Enter a text to check if it's offensive or not.",
+)
+
+# Launch the interface
+iface.launch()