Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+# Load the model and tokenizer from Hugging Face Hub
+model_name = "vai0511/ai-content-classifier"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Define function for classification
+def classify_text(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    logits = outputs.logits
+    predicted_class = torch.argmax(logits, dim=1).item()
+
+    labels = {0: "Human-Written", 1: "AI-Generated", 2: "Paraphrased"}
+    return labels[predicted_class]
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=classify_text,
+    inputs=gr.Textbox(lines=5, placeholder="Enter your text here..."),
+    outputs="text",
+    title="AI-Driven Content Source Identification",
+    description="Detect whether the given text is human-written, AI-generated, or paraphrased."
+)
+
+iface.launch()