Update utils.py

utils.py

@@ -84,63 +84,6 @@ def create_fig(x_min, x_max, to_plot, plot_sentences):
     
     return fig
 
-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 as well
-    as a plot of sentiment over time.
-    """
-
-    customer_sentiments = []
-    plot_sentences = []
-    to_plot = []
-
-    # used to set the x range of ticks on the plot
-    x_min = 100
-    x_max = 0
-
-    for i in range(0, len(diarized), 2):
-        speaker_speech, speaker_id = diarized[i]
-        times, _ = diarized[i + 1]
-
-        sentences = split_into_sentences(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:
-
-            outputs = emotion_pipeline(sentences)
-
-            for idx, (o, t) in enumerate(zip(outputs, sentences)):
-                sent = "neutral"
-                if o["score"] > thresholds[o["label"]]:
-                    customer_sentiments.append(
-                        (t + f"({round(idx*interval_size+start_time,1)} s)", o["label"])
-                    )
-                    if o["label"] in {"joy", "love", "surprise"}:
-                        sent = "positive"
-                    elif o["label"] in {"sadness", "anger", "fear"}:
-                        sent = "negative"
-                if sent != "neutral":
-                    to_plot.append((start_time + idx * interval_size, sent))
-                    plot_sentences.append(t)
-
-            if start_time < x_min:
-                x_min = start_time
-            if end_time > x_max:
-                x_max = end_time
-
-    x_min -= 5
-    x_max += 5
-
-    fig = create_fig(x_min, x_max, to_plot, plot_sentences)
-
-    return customer_sentiments, fig
-
-
 def speech_to_text(speech_file, speaker_segmentation, whisper, alignment_model, metadata, whisper_device):
     speaker_output = speaker_segmentation(speech_file)
     result = whisper.transcribe(speech_file)