Upload folder using huggingface_hub

app/main.py

@@ -52,7 +52,7 @@ app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024  # 16MB limit
 os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
 
 # Configuration
-MODEL_PATH = os.path.join(project_root, "models", "hear_classifier_v8_elite.h5")
+MODEL_PATH = os.path.join(project_root, "models", "hear_classifier_v9_ultimate.h5")
 
 # Global variables for lazy loading
 extractor = None
@@ -66,15 +66,15 @@ def load_resources():
         extractor = HeARExtractor(token=hf_token)
     
     if classifier_model is None:
-        print(f"Loading Elite Model from {MODEL_PATH}...")
+        print(f"Loading Ultimate Model from {MODEL_PATH}...")
         classifier_model = load_model(MODEL_PATH, compile=False)
 
 @app.route('/')
 def index():
     return jsonify({
         "status": "online",
-        "service": "KasaHealth Diagnostic API (Elite V8)",
-        "version": "1.2.0",
+        "service": "KasaHealth Diagnostic API (Ultimate V9)",
+        "version": "1.3.0",
         "message": "Send audio files via POST to /predict"
     })
 

models/hear_classifier_v9_ultimate.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:625a0f1facd74277cdcb33420a5feacf79e8caf2a2b3ce68d052ad199d5bf62a
+size 14685688

test_real_pipeline.py

@@ -19,7 +19,7 @@ from utils.audio_preprocessor import advanced_preprocess
 AUDIO_ROOT = r"c:\Users\ASUS\lung_ai_project\data\coughvid_public\organized"
 SICK_DIR = os.path.join(AUDIO_ROOT, "sick")
 HEALTHY_DIR = os.path.join(AUDIO_ROOT, "healthy")
-MODEL_PATH = os.path.join(PROJECT_ROOT, "models", "hear_classifier_v8_elite.h5")
+MODEL_PATH = os.path.join(PROJECT_ROOT, "models", "hear_classifier_v9_ultimate.h5")
 
 META_PATH = r"c:\Users\ASUS\lung_ai_project\data\coughvid_public\metadata_compiled.csv"
 import pandas as pd
@@ -89,7 +89,7 @@ def test_pipeline():
             prob = classifier_model.predict(X, verbose=0)[0][0]
             
             # 6. API Logic
-            THRESHOLD = 0.80
+            THRESHOLD = 0.50
             if prob > THRESHOLD:
                 final_label = "sick"
             else: