added Readability/ Clarity of speach

app.py

@@ -5,6 +5,7 @@ import torch
 import torch
 import openai
 import whisper
+import textstat
 import datetime
 import requests
 import subprocess
@@ -124,11 +125,20 @@ def speech_to_text_and_sentiment(audio_file, number_of_speakers=2):
         frequency_penalty=0,
         presence_penalty=0
     )
+    
+    readability_score = textstat.flesch_reading_ease(conv)
+    
     call_summary = ""
-    call_summary += f"Sentiment Analysis:\nPositive: {sentiment_scores['pos']} | Negative: {sentiment_scores['neg']} | Neutral: {sentiment_scores['neu']}\n\n"
+    call_summary += "Sentiment Analysis:\n" + "-------------------------------------\n"
+    call_summary += f"Positive: {sentiment_scores['pos']} | Negative: {sentiment_scores['neg']} | Neutral: {sentiment_scores['neu']}\n\n"
+
+    call_summary += "Readability/ Clarity of speach:\n" + "-------------------------------------\n"
+    call_summary += f"Readability Score (Flesch-Kincaid): {readability_score}\n\n"
+
+    call_summary += "Call Summary:\n" + "-------------------------------------\n"
     call_summary += response["choices"][0]["message"]["content"]
 
-    return conv, call_summary
+    return call_summary, conv
 
 
 demo = gr.Interface(
@@ -141,8 +151,9 @@ demo = gr.Interface(
                   default=2, type="number", min=1, max=5)
     ],
     outputs=[
+        gr.Textbox(label="Analysis & Summary")
         gr.Textbox(label="Transcript"),
-        gr.Textbox(label="Analysis")
+        
     ],
     examples=file_names,
     theme=gr.themes.Soft().set(