Update app.py

app.py

@@ -1,30 +1,23 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
 
 from flask import Flask, make_response, render_template, request, jsonify, redirect, url_for, send_from_directory
-from flask_cors import CORS
-import sys
 import os
-import librosa
-import librosa.display
-import numpy as np
-from datetime import date
-import re
-import json
-import email
-import csv
-import datetime
-import smtplib
-import ssl
-from email.mime.text import MIMEText
-import time
+import sys
 import pytz
-import requests
-# import pyaudio
-import wave
+import librosa
 import shutil
+import random
+import string
 import warnings
+import datetime
+import librosa.display
+import numpy as np
 import tensorflow as tf
 import gradio as gr
+
+# import pyaudio
+# import wave
+from tqdm import tqdm
 from keras.models import Sequential
 from keras.layers import Dense
 from keras.utils import to_categorical
@@ -32,7 +25,8 @@ from keras.layers import Flatten, Dropout, Activation
 from keras.layers import Conv2D, MaxPooling2D
 from keras.layers import BatchNormalization
 from sklearn.model_selection import train_test_split
-from tqdm import tqdm
+
+from save_data import flag
 
 warnings.filterwarnings("ignore")
 
@@ -81,41 +75,33 @@ model.load_weights('speech_emotion_detection_ravdess_savee.h5')
 
 
 def selected_audio(audio):
-    if audio and audio != 'Please select any of the following options':
-        post_file_name = audio.lower() + '.wav'
-
-        filepath = os.path.join("pre_recoreded",post_file_name)
-        if os.path.exists(filepath):
-            print("SELECT file name => ",filepath)
-            result = predict_speech_emotion(filepath)
-            print("result = ",result)
-
+    try:
+        if audio and audio != 'Please select any of the following options':
+            post_file_name = audio.lower() + '.wav'
+    
+            filepath = os.path.join("pre_recoreded",post_file_name)
+            if os.path.exists(filepath):
+                print("SELECT file name => ",filepath)
+                result = predict_speech_emotion(filepath)
+                print("result = ",result)
+    
             return result
+    except Exception as e:
+        print(e)
+        return "ERROR"
 
 def recorded_audio(audio):
+    
+    get_audio_name = ''
     try:
-        fileList = os.listdir('recorded_audio') 
-        new_wav_file = ""
-        
-        if(fileList):
-            filename_list = []
-
-            for i in fileList:
-                filename = i.split('.')[0]
-                filename_list.append(int(filename))
+        if audio:
+            get_audio_name = ''.join([random.choice(string.ascii_letters + string.digits) for n in range(5)])
+            audio_file_path = audio.name
+            final_output = predict_speech_emotion(audio_file_path)
             
-            max_file = max(filename_list)
-            new_wav_file = int(max_file) + 1
-        else:
-            new_wav_file="1"
-
-        new_wav_file = str(new_wav_file) + ".wav"
-
-        # filepath = os.path.join('recorded_audio', new_wav_file)
-        # shutil.move(recorded_audio, filepath)
-        filepath = 'recorded_audio/22.wav'
-        result = predict_speech_emotion(audio.name)
-        return result
+            flag(audio_file_path,get_audio_name,final_output)
+        
+        return final_output
     except Exception as e:
         print(e)
         return "ERROR"
@@ -137,31 +123,14 @@ def predict_speech_emotion(filepath):
         return result
 
 
-# demo = gr.Interface(
-#     fn=send_audio, 
-#     inputs=gr.Audio(source="microphone", type="filepath"), 
-#     outputs="text")
-
-# demo.launch()
-
-# selected_audio = gr.Dropdown(["Angry", "Happy", "Sad", "Disgust","Fear", "Surprise", "Neutral"],
-#                             lable = "Input Audio")
-# audio_ui=gr.Audio()
-# text = gr.Textbox()
-# demo = gr.Interface(
-#     fn=send_audio, 
-#     inputs=selected_audio,
-#     outputs=[audio_ui,text])
-
-# demo.launch()
-
 def return_audio_clip(audio_text):
     post_file_name = audio_text.lower() + '.wav'
     filepath = os.path.join("pre_recoreded",post_file_name)
     return filepath
 
 with gr.Blocks(css=".gradio-container {background-color: lightgray;}") as demo:
-    gr.Markdown("Select audio or record audio")
+    gr.Markdown("""<h1 style='text-align: center;>Audio Emotion Detection</h1>""")
+    
     with gr.Row():  
         with gr.Column():  
             input_audio_text = gr.Dropdown(lable="Input Audio",choices=["Please select any of the following options","Angry", "Happy", "Sad", "Disgust","Fear", "Surprise", "Neutral"],interactive=True)