Update app.py

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 import torch
 from charts import spider_chart
+from dictionaries import calculate_average, transform_dict
 from icon import generate_icon
 from transformers import pipeline
 from timestamp import format_timestamp
@@ -29,7 +30,7 @@ def transcribe(file, task, return_timestamps):
     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
@@ -38,8 +39,9 @@ def transcribe(file, task, return_timestamps):
     text = f"<h4>Transcription</h4><div style='overflow-y: scroll; height: 250px;'>{text}</div>"
 
     spider_text = "\n".join(str(feature) for feature in spider_text)
-    fig = spider_chart(classifier, spider_text)  
     
+    fig = spider_chart(calculate_average([transform_dict(classifier.predict(t)[0]) for t in spider_text.split("\n")]))
+
     return file, text, fig
 
 
@@ -74,7 +76,7 @@ article = ("<div style='text-align: center; max-width:800px; margin:10px auto;'>
 
 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=None)#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)