Only Attribute error in Assessment

app.py

@@ -35,25 +35,22 @@ def diff_fn():
     return [('H','+'),('E','-'),('N',None),('\n', None),('F','-'),('Fgo','-'),('M','+')]
 
 def recognizeAudio(audio_file, attributes, is_ipa=False):
-    #print(','.join(attributes))
     global df_output
     if is_ipa:
         p2att_matrix = 'data/p2att_en_us-ipa.csv'
     else:
         p2att_matrix = 'data/p2att_en_us-arpa.csv'
-    output = engine.transcribe(audio_file, attributes= tuple(attributes), phonological_matrix_file=p2att_matrix, human_readable=False)
+    output = engine.transcribe(audio_file, attributes= 'all', phonological_matrix_file=p2att_matrix, human_readable=False)
     records = []
     d = json.loads(output)
     phonemes = d['Phoneme']['symbols']
-    #if is_ipa:
-    #    phonemes = [arpa2ipa[arpa2ipa.arpa==ph].ipa.values[0] for ph in phonemes] 
     records.append(['Phoneme']+phonemes)
     for att in d['Attributes']:
         records.append([att['Name']]+att['Pattern'])
     df = pd.DataFrame.from_records(records)
     df.fillna('', inplace=True)
     df_output = df
-    return df.to_html(header=False, index=False)
+    return df[df[0].isin(['Phoneme']+list(attributes))].to_html(header=False, index=False)
 
  
 #Get error by matching the expected sequence with the recognized one and return the output in a format that can be visualized by the gradio HighlightedText box
@@ -159,8 +156,6 @@ with gr.Blocks() as gui:
         
         process.click(fn=recognizeAudio, inputs=[record_audio,att_list, is_ipa], outputs=recognition)
         
-    
-        
     with gr.Tab("Assessment"):
         assess = gr.Button("Assessment")
         diff = []
@@ -171,24 +166,26 @@ with gr.Blocks() as gui:
                     color_map={"S": "red", "I": "green", "D":"blue"}, visible=False))
             
         def get_assessment(prompt_phonemes):#, recognized_phonemes, recognized_attributes):
-            outputs = [gr.HighlightedText(visible=False)]*(len(Attributes)+1)
+            outputs = [gr.HighlightedText(visible=False)]*(df_output.shape[0])
             outputs[0] = gr.HighlightedText(label=f"Phoneme Assessment",
                                               value=get_error(prompt_phonemes.split(), df_output.iloc[0].values[1:]),
                                               visible=True)
             i = 1
-            for i,r in df_output.iloc[1:].iterrows():
+            for j,r in df_output.iloc[1:].iterrows():
                 convert = lambda ph: '-' if f'n_{att}' in engine.p2att_map[ph] else '+'
                 att = r.iloc[0]
                 exp_att = [convert(ph) for ph in prompt_phonemes.split()]
                 rec_att = r.iloc[1:].values
-                outputs[i] = gr.HighlightedText(label=f"{att} Assessment",
+                if ''.join(exp_att) != ''.join(rec_att):
+                    outputs[i] = gr.HighlightedText(label=f"{att} Assessment",
                                               value=get_error(exp_att, rec_att),
                                               visible=True)
-                i += 1
+                    i += 1
                 
             return outputs
 
-        assess.click(fn=get_assessment, inputs= [prompt_phonemes], outputs=diff)
+        assess.click(fn=get_assessment, inputs= [prompt_phonemes], outputs=diff)    
+        
 
     with gr.Tab("Analysis"):
         selected_att = gr.Dropdown( sorted(Attributes), label="Select an Attribute to plot", value='voiced', interactive=True)