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.gitattributes

@@ -33,3 +33,12 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
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+examples/116_1b2_Pl_sc_Meditron.wav filter=lfs diff=lfs merge=lfs -text
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+examples/135_2b2_Al_mc_LittC2SE.wav filter=lfs diff=lfs merge=lfs -text
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README.md

@@ -1,14 +1,14 @@
----
-title: LungDiseaseDetector
-emoji: 🔥
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 5.7.1
-app_file: app.py
-pinned: false
-license: mit
-short_description: 'AI To Detect Lung Diseases from just Lung Sounds '
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: LungDiseaseDetector
+emoji: 🔥
+colorFrom: green
+colorTo: pink
+sdk: gradio
+sdk_version: 5.7.1
+app_file: app.py
+pinned: false
+license: mit
+short_description: 'AI To Detect Lung Diseases from just Lung Sounds '
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,95 @@
+import librosa
+import tensorflow as tf
+import soundfile
+import gradio as gr 
+
+import pandas as pd
+import os
+import random
+import numpy as np  
+# Set the environment variable
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+audio_files_path = 'examples'
+c_names = ['Bronchiectasis', 'COPD', 'Healthy', 'Pneumonia', 'URTI']
+
+# Loading Audio Files
+audio_files = [] 
+for file in os.listdir(audio_files_path):
+    if file.endswith('.wav'): 
+        audio_files.append(file)  
+# convert the list to a df 
+audio_files_df = pd.DataFrame(audio_files, columns=['audio_paths'])
+# print(audio_files_df.iloc[0]['audio_file']) 
+audio_files_to_show = audio_files_df['audio_paths'].tolist() 
+# create a gradio interface
+# 0. Load models 
+# 1. Audio File input
+# 2. clear and Submit button
+# 3. Upon submit , first preprocess the audio file using log mel and then run the outputs through the AI model 
+# # 4. Output the prediction
+
+def load_model():
+    # Load the model  
+    return tf.keras.models.load_model("models/lung_disease_predictor_cnn_logmel_without_data_augmentation.keras")
+
+def preprocessing(audio_file, mode):
+    # we want to resample audio to 16 kHz
+    sr_new = 16000 # 16kHz sample rate
+    x, sr = librosa.load(audio_file, sr=sr_new) 
+    # padding sound 
+    # because duration of sound is dominantly 20 s and all of sample rate is 22050
+    # we want to pad or truncated sound which is below or above 20 s respectively
+    max_len = 5 * sr_new  # length of sound array = time x sample rate
+    if x.shape[0] < max_len:
+      # padding with zero
+      pad_width = max_len - x.shape[0]
+      x = np.pad(x, (0, pad_width))
+    elif x.shape[0] > max_len:
+      # truncated
+      x = x[:max_len]
+    
+    if mode == 'mfcc':
+      feature = librosa.feature.mfcc(y=x, sr=sr_new)
+    
+    elif mode == 'log_mel':
+      feature = librosa.feature.melspectrogram(y=x, sr=sr_new, n_mels=128, fmax=8000)
+      feature = librosa.power_to_db(feature, ref=np.max) 
+
+    return feature
+
+
+def predict_lung_disease(audio_data):
+    # Create a temporary file
+    
+    filename = "temp/lungs_audio.wav"  # Set your desired filename 
+    soundfile.write(filename, audio_data[1],samplerate=audio_data[0])  # Save audio to file
+    
+    # Process the temporary audio file
+    processed_audio = preprocessing(filename, 'log_mel').reshape((-1, 128, 157, 1)) 
+    new_preds = model.predict(processed_audio) 
+    new_classpreds = np.argmax(new_preds, axis=1)
+    print(str(c_names[new_classpreds[0]]))
+    return str(c_names[new_classpreds[0]])
+
+# Gradio Interface
+model = load_model() 
+
+# have example audio files to test 
+
+ 
+
+# Interface
+iface = gr.Interface(
+    fn=predict_lung_disease,
+    inputs=["audio"],
+    outputs="text",
+    title="VitalSenseAI", 
+    examples=
+        [  
+           [os.path.join(audio_files_path,audio_file)] for audio_file in audio_files_to_show
+        ]
+    )
+ 
+
+iface.launch()

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models/models_info.txt

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+# lung_disease_predictor_cnn_mcff_without_data_augmentation.keras 
+-> 95% Training Acc. , 85% Testing Acc.
+
+# lung_disease_predictor_cnn_mcff_with_data_augmentation
+-> Training Accuracy:  90%
+-> Testing Accuracy:  55% 
+
+# lung_disease_predictor_cnn_logmel_without_data_augmentation
+-> Training Accuracy: 93%
+-> Testing Accuracy: 90% 
+