Update app.py

app.py

@@ -29,10 +29,10 @@ def get_predictions(logits, indexes):
             return ind, probs
 
 
-def return_pred_name(name_dict, ind):
+def return_pred_name(names, ind):
     out = []
     for i in ind:
-        out.append(name_dict[i])
+        out.append(names[i])
     return out
 
 
@@ -49,7 +49,7 @@ def predict(title, summary, model, tokenizer):
     prediction = []
     prediction_probs = []
     while sum_probs < 0.95:
-        prediction.append(name_dict[classes[ind]])
+        prediction.append(names[classes[ind]])
         prediction_probs.append(str("{:.2f}".format(100 * probs[classes[ind]])) + "%")
         sum_probs += probs[classes[ind]]
         ind += 1
@@ -61,28 +61,27 @@ def get_results(prediction, prediction_probs):
     frame =  pd.DataFrame({'Category': prediction, 'Confidence': prediction_probs})
     frame.index = np.arange(1, len(frame) + 1)
     return frame
+   
     
-name_dict = {4: 'cs',
- 19: 'stat',
- 1: 'astro-ph',
- 16: 'q-bio',
- 6: 'eess',
- 3: 'cond-mat',
- 12: 'math',
- 15: 'physics',
- 18: 'quant-ph',
- 17: 'q-fin',
- 7: 'gr-qc',
- 13: 'nlin',
- 2: 'cmp-lg',
- 5: 'econ',
- 8: 'hep-ex',
- 11: 'hep-th',
- 14: 'nucl-th',
- 10: 'hep-ph',
- 9: 'hep-lat',
- 0: 'adap-org'}
-
+names = {3: 'cs',
+         18: 'stat',
+         10: 'math',
+         14: 'physics',
+         15: 'q-bio',
+         0: 'astro-ph',             
+         2: 'cond-mat',
+         17: 'quant-ph',            
+         5: 'eess',       
+         1: 'cmp-lg',
+         8: 'hep-ph',             
+         6: 'gr-qc',
+         9: 'hep-th',             
+         12: 'nlin',            
+         4: 'econ'  
+         16: 'q-fin',             
+         7: 'hep-ex',            
+         11: 'math-ph',
+         13: 'nucl-th'}      
 
 
 st.title("Find out the topic of the article without reading!")