Sync updated app from GitHub

app.py

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+# app.py
+
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load model and tokenizer
+model_name = "hamzab/roberta-fake-news-classification"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# Prediction function
+def predict_fake(title, text):
+    input_str = f"<title>{title}<content>{text}<end>"
+    inputs = tokenizer.encode_plus(
+        input_str,
+        max_length=512,
+        padding="max_length",
+        truncation=True,
+        return_tensors="pt"
+    )
+    with torch.no_grad():
+        outputs = model(
+            inputs["input_ids"].to(device),
+            attention_mask=inputs["attention_mask"].to(device)
+        )
+    probs = torch.nn.functional.softmax(outputs.logits, dim=1)[0]
+    return {"Fake": float(probs[0]), "Real": float(probs[1])}
+
+# Gradio interface
+iface = gr.Interface(
+    fn=predict_fake,
+    inputs=[
+        gr.Textbox(label="Title"),
+        gr.Textbox(label="Content", lines=6)
+    ],
+    outputs=gr.Label(num_top_classes=2),
+    title="Fake News Detector",
+    description="Enter a news headline and content to classify as Real or Fake using a RoBERTa model."
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+torch
+transformers
+gradio