Create app.py

app.py

@@ -0,0 +1,45 @@
+import gradio as gr
+from huggingface_hub import from_pretrained_keras
+m = from_pretrained_keras('sgonzalezsilot/FakeNews-Detection-Twitter-Thesis')
+
+def bert_encode(tokenizer,data,maximum_length) :
+  input_ids = []
+  attention_masks = []
+  
+
+  for i in range(len(data)):
+      encoded = tokenizer.encode_plus(
+        
+        data[i],
+        add_special_tokens=True,
+        max_length=maximum_length,
+        pad_to_max_length=True,
+        truncation = True,
+        return_attention_mask=True,
+      )
+      
+      input_ids.append(encoded['input_ids'])
+      attention_masks.append(encoded['attention_mask'])
+
+  return np.array(input_ids),np.array(attention_masks)
+    
+train_encodings = tokenizer(train_texts, truncation=True, padding=True)
+test_encodings = tokenizer(test_texts, truncation=True, padding=True)
+
+MODEL = "digitalepidemiologylab/covid-twitter-bert-v2"
+tokenizer, roberta_model = getTokenizerAndModel(MODEL, model_normalization=False, from_pt = False, regularization=False)
+
+sentence_length = 110
+train_input_ids,train_attention_masks = bert_encode(tokenizer,train_texts,sentence_length)
+test_input_ids,test_attention_masks = bert_encode(tokenizer,test_texts,sentence_length)
+    
+def get_news(input_text):
+  return sentiment(input_text)
+
+iface = gr.Interface(fn = get_news, 
+                     inputs = "text", 
+                     outputs = ['text'],
+                     title = 'Fake News', 
+                     description="")
+                     
+iface.launch(inline = False)