Update app.py

app.py

@@ -18,7 +18,7 @@ def bulk_function(filename):
           return {k: v[idx] for k, v in self.tokenized_texts.items()}
 
   # load tokenizer and model, create trainer
-  model_name = "j-hartmann/emotion-english-distilroberta-base"
+  model_name = "j-hartmann/MindMiner-Binary"
   tokenizer = AutoTokenizer.from_pretrained(model_name)
   model = AutoModelForSequenceClassification.from_pretrained(model_name)
   trainer = Trainer(model=model)  
@@ -78,29 +78,20 @@ def bulk_function(filename):
   temp = (np.exp(predictions[0])/np.exp(predictions[0]).sum(-1,keepdims=True))
 
   # container
-  anger = []
-  disgust = []
-  fear = []
-  joy = []
-  neutral = []
-  sadness = []
-  surprise = []
+  high = []
+  low = []
 
   # extract scores (as many entries as exist in pred_texts)
   for i in range(len(lines_s)):
-    anger.append(round(temp[i][0], 3))
-    disgust.append(round(temp[i][1], 3))
-    fear.append(round(temp[i][2], 3))
-    joy.append(round(temp[i][3], 3))
-    neutral.append(round(temp[i][4], 3))
-    sadness.append(round(temp[i][5], 3))
-    surprise.append(round(temp[i][6], 3))
+    high.append(round(temp[i][0], 3))
+    low.append(round(temp[i][1], 3))
+
 
   # define df
-  df = pd.DataFrame(list(zip(ids,lines_s,labels,scores_rounded, anger, disgust, fear, joy, neutral, sadness, surprise)), columns=[df_input.columns[0], df_input.columns[1],'max_label','max_score', 'anger', 'disgust', 'fear', 'joy', 'neutral', 'sadness', 'surprise'])
+  df = pd.DataFrame(list(zip(ids,lines_s,labels,scores_rounded, high, low)), columns=[df_input.columns[0], df_input.columns[1],'max_label','max_score', 'high', 'low'])
   print(df)
   # save results to csv
-  YOUR_FILENAME = filename.name.split(".")[0] + "_emotion_predictions" + ".csv"  # name your output file
+  YOUR_FILENAME = filename.name.split(".")[0] + "_MindMiner_Predictions" + ".csv"  # name your output file
   df.to_csv(YOUR_FILENAME, index=False)
 
   # return dataframe for space output