Update app.py

app.py

@@ -1,3 +1,19 @@
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("hamzab/roberta-fake-news-classification")
+
+model = AutoModelForSequenceClassification.from_pretrained("hamzab/roberta-fake-news-classification")
+
 import gradio as gr
 
-gr.load("models/hamzab/roberta-fake-news-classification").launch()
+import torch
+def predict_fake(title,text):
+    input_str = "<title>" + title + "<content>" +  text + "<end>"
+    input_ids = tokenizer.encode_plus(input_str, max_length=512, padding="max_length", truncation=True, return_tensors="pt")
+    device =  'cuda' if torch.cuda.is_available() else 'cpu'
+    model.to(device)
+    with torch.no_grad():
+        output = model(input_ids["input_ids"].to(device), attention_mask=input_ids["attention_mask"].to(device))
+    return dict(zip(["Fake","Real"], [x.item() for x in list(torch.nn.Softmax()(output.logits)[0])] ))
+
+iface = gr.Interface(fn=predict_fake, inputs=[gr.inputs.Textbox(lines=1,label="headline"),gr.inputs.Textbox(lines=6,label="content")], outputs="label").launch(share=True)