Update app.py

app.py

@@ -5,6 +5,9 @@ import skops.io as sio
 import gradio as gr
 import warnings
 
+def remove_warnings():
+    warnings.filterwarnings('ignore')
+    
 ## Voice Data Feature Extraction
 
 ### extract the features from the audio files using mfcc
@@ -16,42 +19,43 @@ def feature_extracter(fileName):
     return list(mfccs_scaled_features)
 
 def prediction_age_gender(fileName):
+        
+      remove_warnings()  
+      col_name = ['Feature_1', 'Feature_2', 'Feature_3', 'Feature_4', 'Feature_5','Feature_6', 'Feature_7', 'Feature_8', 'Feature_9', 'Feature_10','Feature_11', 'Feature_12', 'Feature_13', 'Feature_14', 'Feature_15','Feature_16', 'Feature_17', 'Feature_18', 'Feature_19', 'Feature_20','Feature_21', 'Feature_22', 'Feature_23', 'Feature_24', 'Feature_25','Feature_26', 'Feature_27', 'Feature_28', 'Feature_29', 'Feature_30']
+
+      observation = [feature_extracter(fileName)]
+      observation = pd.DataFrame(observation, columns = col_name)
+
+      ## scaling the observation
+      scaler = sio.load('scaler')
+      scaled_observation = scaler.transform(observation)
+      scaled_observation = pd.DataFrame(scaled_observation, columns = col_name)  
 
-  col_name = ['Feature_1', 'Feature_2', 'Feature_3', 'Feature_4', 'Feature_5','Feature_6', 'Feature_7', 'Feature_8', 'Feature_9', 'Feature_10','Feature_11', 'Feature_12', 'Feature_13', 'Feature_14', 'Feature_15','Feature_16', 'Feature_17', 'Feature_18', 'Feature_19', 'Feature_20','Feature_21', 'Feature_22', 'Feature_23', 'Feature_24', 'Feature_25','Feature_26', 'Feature_27', 'Feature_28', 'Feature_29', 'Feature_30']
-
-  observation = [feature_extracter(fileName)]
-  observation = pd.DataFrame(observation, columns = col_name)
-
-  ## scaling the observation
-  scaler = joblib.load('/content/scaler.pkl')
-  scaled_observation = scaler.transform(observation)
-  scaled_observation = pd.DataFrame(scaled_observation, columns = col_name)  
-
-  ### Gender classification model
-  gender_model = joblib.load('/content/KNN_gender_detection.pkl')
-  gender_predict = gender_model.predict_proba(scaled_observation)
-  ## considering the labels 1 = male 0 = female
-  gender_dict = {}
-  gender_dict['Female'] = gender_predict[0][0]
-  gender_dict['Male'] = gender_predict[0][1]
-
-  ### Age classification model
-  age_model = joblib.load('/content/KNN_age_model.pkl')
-  age_predict = age_model.predict_proba(scaled_observation)
-  age_dict = {}
-  age_dict['Eighties'] = age_predict[0][0]
-  age_dict['Fifties'] = age_predict[0][1]
-  age_dict['Fourties'] = age_predict[0][2]
-  age_dict['Seventies'] = age_predict[0][3]
-  age_dict['Sixties'] = age_predict[0][4]
-  age_dict['Teens'] = age_predict[0][5]
-  age_dict['Thirties'] = age_predict[0][6]
-  age_dict['Twenties'] = age_predict[0][7]
-  age_dict['Other'] = 1 - age_dict['Eighties'] - age_dict['Fifties'] - age_dict['Fourties'] - age_dict['Seventies'] - age_dict['Sixties']  - age_dict['Teens'] - age_dict['Thirties'] - age_dict['Twenties']
-
-  #final = "The person is a: " + gender + " of the age group: " + age
-  return gender_dict, age_dict
+      ### Gender classification model
+      gender_model = sio.load('KNN_gender_detection')
+      gender_predict = gender_model.predict_proba(scaled_observation.values)
+      ## considering the labels 1 = male 0 = female
+      gender_dict = {}
+      gender_dict['Female'] = gender_predict[0][0]
+      gender_dict['Male'] = gender_predict[0][1]
 
+      ### Age classification model
+      age_model = sio.load('KNN_age_model')
+      age_predict = age_model.predict_proba(scaled_observation.values)
+      age_dict = {}
+      age_dict['Eighties'] = age_predict[0][0]
+      age_dict['Fifties'] = age_predict[0][1]
+      age_dict['Fourties'] = age_predict[0][2]
+      age_dict['Seventies'] = age_predict[0][3]
+      age_dict['Sixties'] = age_predict[0][4]
+      age_dict['Teens'] = age_predict[0][5]
+      age_dict['Thirties'] = age_predict[0][6]
+      age_dict['Twenties'] = age_predict[0][7]
+      age_dict['Other'] = 1 - age_dict['Eighties'] - age_dict['Fifties'] - age_dict['Fourties'] - age_dict['Seventies'] - age_dict['Sixties']  - age_dict['Teens'] - age_dict['Thirties'] - age_dict['Twenties']
+
+      #final = "The person is a: " + gender + " of the age group: " + age
+      return gender_dict, age_dict
+    
 demo = gr.Interface(
     prediction_age_gender,
     inputs = [gr.Audio(sources=["microphone","upload"], type = 'filepath')],