Update app.py

app.py

@@ -53,46 +53,24 @@ speech_to_text = partial(
     )
 
 def summarize(diarized, summarization_pipeline):
-    """
-    diarized: a list of tuples. Each tuple has a string to be displayed and a label for highlighting.
-        The start/end times are not highlighted [(speaker text, speaker id), (start time/end time, None)]
-    check is a list of speaker ids whose speech will get summarized
-    """
-
     text = ""
     for d in diarized:
         text += f"\n{d[1]}: {d[0]}"
 
-    return call_summarize_api(text)
+    return summarization_pipeline(text)[0]["summary_text"]
 
 def sentiment(diarized, emotion_pipeline):
-    """
-    diarized: a list of tuples. Each tuple has a string to be displayed and a label for highlighting.
-        The start/end times are not highlighted [(speaker text, speaker id), (start time/end time, None)]
-
-    This function gets the customer's sentiment and returns a list for highlighted text.
-    """
-
     customer_sentiments = []
 
     for i in range(0, len(diarized), 2):
         speaker_speech, speaker_id = diarized[i]
-        times, _ = diarized[i + 1]
-
         sentences = split(speaker_speech)
-        
-        start_time, end_time = times[5:].split("-")
-        start_time, end_time = float(start_time), float(end_time)
-        interval_size = (end_time - start_time) / len(sentences)
-        if "Customer" in speaker_id:
 
+        if "Customer" in speaker_id:
             outputs = emotion_pipeline(sentences)
-
             for idx, (o, t) in enumerate(zip(outputs, sentences)):
                 if o["score"] > thresholds[o["label"]]:
-                    customer_sentiments.append(
-                        (t + f"({round(idx*interval_size+start_time,1)} s)", o["label"])
-                    )
+                    customer_sentiments.append((t, o["label"]))
 
     return customer_sentiments