Create app.py

app.py

@@ -0,0 +1,27 @@
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import gradio as gr
+
+# Load pretrained hate-speech model
+MODEL_NAME = "Hate-speech-CNERG/dehatebert-mono-english"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+
+def detect_hate(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    outputs = model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    labels = ["non-hate", "hate"]
+    result = {labels[i]: float(probs[0][i]) for i in range(len(labels))}
+    return result
+
+# Gradio interface
+demo = gr.Interface(
+    fn=detect_hate,
+    inputs=gr.Textbox(label="Enter Text", placeholder="Type something..."),
+    outputs=gr.Label(label="Prediction"),
+    title="🧠 Hate Speech Detector",
+    description="Classifies text as hate or non-hate using a fine-tuned BERT model.",
+)
+
+demo.launch()