Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForTokenClassification,RobertaTokenizer
+import torch
+from fin_readability_sustainability import BERTClass, do_predict
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+tokenizer_sus = RobertaTokenizer.from_pretrained('roberta-base')
+model_sustain = BERTClass(2, "sustanability")
+model_sustain.to(device)
+model_sustain.load_state_dict(torch.load('sustainability_model.bin', map_location=device)['model_state_dict'])
+
+
+from nltk.tokenize import sent_tokenize
+def get_sustainability(text):
+  df = pd.DataFrame({'sentence':sent_tokenize(text)})
+  actual_predictions_sustainability = do_predict(model_sustain, tokenizer_sus, df)
+  highlight = []
+  for sent, prob in zip(df['sentence'].values, actual_predictions_sustainability[1]):
+    if prob>=4.384316:
+      highlight.append((sent, 'non-sustainable'))
+    elif prob<=1.423736:
+      highlight.append((sent, 'sustainable'))
+    else:
+      highlight.append((sent, '-'))
+  return highlight
+
+
+
+ b6 = gr.Button("Get Sustainability")
+              b6.click(get_sustainability, inputs = text, outputs = gr.HighlightedText())
+              
+              
+iface = gr.Interface(fn=get_sustainability, inputs=gr.inputs.Textbox(lines=5, placeholder="Enter Financial Text here..."), title="CONBERT",description="SUSTAINABILITY TOOL", outputs=gr.HighlightedText(), allow_flagging="never")
+iface.launch()