Fix HF Spaces deployment: Add proper path handling and model loading

Dockerfile

@@ -15,8 +15,13 @@ RUN pip install --no-cache-dir -r requirements.txt
 # Create necessary directories with proper permissions
 RUN mkdir -p /code/static/uploads \
     && mkdir -p /code/logs \
+    && mkdir -p /code/external/BodybuildingPoseClassifier \
     && chmod -R 777 /code/static/uploads \
-    && chmod -R 777 /code/logs
+    && chmod -R 777 /code/logs \
+    && chmod -R 777 /code/external
+
+# Copy the model file first
+COPY external/BodybuildingPoseClassifier/bodybuilding_pose_classifier.h5 /code/external/BodybuildingPoseClassifier/
 
 # Copy the rest of the application
 COPY . .

app.py

@@ -49,90 +49,52 @@ file_handler = logging.FileHandler(os.path.join(log_dir, 'app.log'))
 file_handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
 logger.addHandler(file_handler)
 
-def log_memory_usage():
-    """Log current memory usage."""
-    try:
-        process = psutil.Process()
-        memory_info = process.memory_info()
-        logger.info(f"Memory usage: {memory_info.rss / 1024 / 1024:.2f} MB")
-    except Exception as e:
-        logger.error(f"Error logging memory usage: {e}")
-
-def cleanup_memory():
-    """Force garbage collection and log memory usage."""
-    try:
-        gc.collect()
-        log_memory_usage()
-    except Exception as e:
-        logger.error(f"Error in cleanup_memory: {e}")
-
-def wrap_text(text: str, font_face: int, font_scale: float, thickness: int, max_width: int) -> list[str]:
-    """Wrap text to fit within max_width."""
-    if not text:
-        return []
-
-    lines = []
-    words = text.split(' ')
-    current_line = ''
-
-    for word in words:
-        # Check width if current_line + word fits
-        test_line = current_line + word + ' '
-        (text_width, _), _ = cv2.getTextSize(test_line.strip(), font_face, font_scale, thickness)
-
-        if text_width <= max_width:
-            current_line = test_line
-        else:
-            # Word doesn't fit, so current_line (without the new word) is a complete line
-            lines.append(current_line.strip())
-            # Start new line with the current word
-            current_line = word + ' '
-            # If a single word is too long, it will still overflow. Handle by breaking word if necessary (future enhancement)
-            (single_word_width, _), _ = cv2.getTextSize(word.strip(), font_face, font_scale, thickness)
-            if single_word_width > max_width:
-                # For now, just add the long word and let it overflow, or truncate it.
-                # A more complex solution would break the word.
-                lines.append(word.strip()) # Add the long word as its own line
-                current_line = '' # Reset current_line as the long word is handled
-
-    if current_line.strip(): # Add the last line
-        lines.append(current_line.strip())
-    
-    return lines if lines else [text] # Ensure at least the original text is returned if no wrapping happens
-
-app = Flask(__name__, static_url_path='/static', static_folder='static')
+# Define base paths
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+STATIC_DIR = os.path.join(BASE_DIR, 'static')
+UPLOAD_DIR = os.path.join(STATIC_DIR, 'uploads')
+MODEL_DIR = os.path.join(BASE_DIR, 'external', 'BodybuildingPoseClassifier')
+
+# Ensure all required directories exist
+for directory in [STATIC_DIR, UPLOAD_DIR, MODEL_DIR, log_dir]:
+    os.makedirs(directory, exist_ok=True)
+    logger.info(f"Ensured directory exists: {directory}")
+
+app = Flask(__name__, static_url_path='/static', static_folder=STATIC_DIR)
 CORS(app, resources={r"/*": {"origins": "*"}})
 
-app.config['UPLOAD_FOLDER'] = 'static/uploads'
+app.config['UPLOAD_FOLDER'] = UPLOAD_DIR
 app.config['MAX_CONTENT_LENGTH'] = 100 * 1024 * 1024  # 100MB max file size
 
-# Ensure upload directory exists
-os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
-
 # Load CNN model for bodybuilding pose classification
 try:
     logger.info("Loading CNN model...")
-    cnn_model_path = 'external/BodybuildingPoseClassifier/bodybuilding_pose_classifier.h5'
+    cnn_model_path = os.path.join(MODEL_DIR, 'bodybuilding_pose_classifier.h5')
+    logger.info(f"Looking for model at: {cnn_model_path}")
+    
     if not os.path.exists(cnn_model_path):
         raise FileNotFoundError(f"CNN model not found at {cnn_model_path}")
-    cnn_model = load_model(cnn_model_path)
+    
+    # Load model with custom_objects to handle any custom layers
+    cnn_model = load_model(cnn_model_path, compile=False)
     logger.info("CNN model loaded successfully")
 except Exception as e:
     logger.error(f"Error loading CNN model: {e}")
+    logger.error(traceback.format_exc())
     raise
 
 cnn_class_labels = ['side_chest', 'front_double_biceps', 'back_double_biceps', 'front_lat_spread', 'back_lat_spread']
 
 def predict_pose_cnn(img_path):
     try:
-        cleanup_memory()  # Clean up before prediction
+        cleanup_memory()
         if gpus:
             logger.info("[CNN_DEBUG] Using GPU for CNN prediction")
             with tf.device('/GPU:0'):
                 img = image.load_img(img_path, target_size=(150, 150))
                 img_array = image.img_to_array(img)
                 img_array = np.expand_dims(img_array, axis=0) / 255.0
-                predictions = cnn_model.predict(img_array, verbose=0)  # Disable progress bar
+                predictions = cnn_model.predict(img_array, verbose=0)
                 predicted_class = np.argmax(predictions, axis=1)
                 confidence = float(np.max(predictions))
         else:
@@ -141,7 +103,7 @@ def predict_pose_cnn(img_path):
                 img = image.load_img(img_path, target_size=(150, 150))
                 img_array = image.img_to_array(img)
                 img_array = np.expand_dims(img_array, axis=0) / 255.0
-                predictions = cnn_model.predict(img_array, verbose=0)  # Disable progress bar
+                predictions = cnn_model.predict(img_array, verbose=0)
                 predicted_class = np.argmax(predictions, axis=1)
                 confidence = float(np.max(predictions))
         
@@ -149,10 +111,10 @@ def predict_pose_cnn(img_path):
         return cnn_class_labels[predicted_class[0]], confidence
     except Exception as e:
         logger.error(f"[CNN_ERROR] Exception during CNN prediction: {e}")
-        traceback.print_exc()
+        logger.error(traceback.format_exc())
         raise
     finally:
-        cleanup_memory()  # Clean up after prediction
+        cleanup_memory()
 
 @app.route('/static/uploads/<path:filename>')
 def serve_video(filename):