Update app.py

app.py

@@ -1,5 +1,6 @@
 import gradio as gr
 import torch
+from charts import spider_chart
 from icon import generate_icon
 from transformers import pipeline
 from timestamp import format_timestamp
@@ -16,20 +17,30 @@ pipe = pipeline(
     device=device,
 )
 
+#Define classifier for sentiment analysis
+classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", top_k=None)
+
 def transcribe(file, task, return_timestamps):
     outputs = pipe(file, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)
     text = outputs["text"]
     timestamps = outputs["chunks"]
 
+    #If return timestamps is True, return html text with timestamps format
     if return_timestamps==True:
+      spider_text = [f"{chunk['text']}" for chunk in timestamps] #Text for spider chart without timestamps
       timestamps = [f"[{format_timestamp(chunk['timestamp'][0])} -> {format_timestamp(chunk['timestamp'][1])}] {chunk['text']}" for chunk in timestamps]
-
+      
     else:
       timestamps = [f"{chunk['text']}" for chunk in timestamps]
+      spider_text = timestamps
 
     text = "<br>".join(str(feature) for feature in timestamps)
     text = f"<h4>Transcription</h4><div style='overflow-y: scroll; height: 250px;'>{text}</div>"
-    return file, text
+
+    spider_text = "\n".join(str(feature) for feature in spider_text)
+    fig = spider_chart(classifier, spider_text)  
+    
+    return file, text, fig
 
 
 inputs = [gr.Audio(source="upload", label="Audio file", type="filepath"),
@@ -37,7 +48,8 @@ inputs = [gr.Audio(source="upload", label="Audio file", type="filepath"),
         gr.Checkbox(value=True, label="Return timestamps")]
 
 outputs = [gr.Audio(label="Processed Audio", type="filepath"),
-        gr.outputs.HTML("text")]
+        gr.outputs.HTML("text"),
+           gr.Plot(label="fig")]
 
 title = "Whisper Demo: Transcribe Audio"
 
@@ -47,7 +59,8 @@ description = ("Transcribe long-form audio inputs with the click of a button! De
         f" checkpoint [{MODEL_NAME1}](https://huggingface.co/{MODEL_NAME1}) and 🤗 Transformers to transcribe audio files"
         " of arbitrary length. Check some of the 'cool' examples below")
 
-examples = [["When a Demon Defiles a Witch.wav","transcribe",True]]
+examples = [["When a Demon Defiles a Witch.wav","transcribe",True]
+            ["Immaculate Misconception.wav","transcribe", True]]
 
 
 linkedin = generate_icon("linkedin")
@@ -58,10 +71,10 @@ article = ("<div style='text-align: center; max-width:800px; margin:10px auto;'>
             f"{github} <a href='https://github.com/jpdiazpardo' target='_blank'>jpdiazpardo</a></p>"
           )
 
+
 title = "Scream: Fine-Tuned Whisper model for automatic gutural speech recognition 🤟🤟🤟"
 
-demo = gr.Interface(title = title, fn=transcribe, inputs = inputs, outputs = outputs, description=description, cache_examples=True, 
-                    allow_flagging="never", article = article , examples=examples)
+demo = gr.Interface(title = title, fn=transcribe, inputs = inputs, outputs = outputs, description=description, cache_examples=True, allow_flagging="never", article = article , examples=examples)
 
 demo.queue(concurrency_count=3)
 demo.launch(debug = True)