Make all debug prints conditional on get_debug() - suppress debug output when --debug not specified

vidai/worker_analysis.py

@@ -183,6 +183,7 @@ def check_job_cancelled(job_id):
 
 def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None, comm=None):
     """Analyze media using dynamic model loading."""
+    if get_debug():
         print(f"DEBUG: Starting analyze_media for job {job_id_int}, media_path={media_path}")
 
     # Send initial progress update
@@ -201,8 +202,10 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
     total_tokens = 0
 
     # Get model with reference counting
+    if get_debug():
         print(f"DEBUG: Loading model {model_path} for job {job_id_int}")
     model = get_or_load_model(model_path)
+    if get_debug():
         print(f"DEBUG: Model loaded for job {job_id_int}")
 
     # Send progress update after model loading
@@ -217,6 +220,7 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
         print(f"PROGRESS: Job {job_id_int} - 8% - Model loaded successfully")
 
     # Get system prompt
+    if get_debug():
         print(f"DEBUG: Retrieving system prompt for job {job_id_int}")
     try:
         from .config import get_system_prompt_content
@@ -224,9 +228,11 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
         full_prompt = system_prompt + " " + prompt if system_prompt else prompt
     except:
         full_prompt = prompt
+    if get_debug():
         print(f"DEBUG: Full prompt set for job {job_id_int}")
 
     if is_video(media_path):
+        if get_debug():
             print(f"DEBUG: Detected video, extracting frames for job {job_id_int}")
         frames, output_dir = extract_frames(media_path, interval, optimize=True)
         total_frames = len(frames)
@@ -245,6 +251,7 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
         descriptions = []
 
         for i, (frame_path, ts) in enumerate(frames):
+            if get_debug():
                 print(f"DEBUG: Processing frame {i+1}/{total_frames} at {ts:.2f}s for job {job_id_int}")
 
             # Send progress update before processing
@@ -261,6 +268,7 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
 
             # Check for cancellation
             if job_id_int and check_job_cancelled(job_id_int):
+                if get_debug():
                     print(f"DEBUG: Job {job_id_int} cancelled during frame processing")
                 # Clean up and return cancelled message
                 for fp, _ in frames[i:]:
@@ -278,6 +286,7 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
 
             desc, tokens = analyze_single_image(frame_path, full_prompt, model)
             total_tokens += tokens
+            if get_debug():
                 print(f"DEBUG: Frame {i+1} analyzed for job {job_id_int}")
             descriptions.append(f"At {ts:.2f}s: {desc}")
             os.unlink(frame_path)
@@ -308,6 +317,7 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
             import shutil
             shutil.rmtree(output_dir)
 
+        if get_debug():
             print(f"DEBUG: All frames processed, generating summary for job {job_id_int}")
 
         # Send progress update for summary generation
@@ -323,6 +333,7 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
 
         # Check for cancellation before summary
         if job_id_int and check_job_cancelled(job_id_int):
+            if get_debug():
                 print(f"DEBUG: Job {job_id_int} cancelled before summary")
             return "Job cancelled by user", total_tokens
 
@@ -361,6 +372,7 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
                 summary_tokens = 0
 
         total_tokens += summary_tokens
+        if get_debug():
             print(f"DEBUG: Summary generated for job {job_id_int}")
 
         # Send final progress update
@@ -377,6 +389,7 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
         result = f"Frame Descriptions:\n" + "\n".join(descriptions) + f"\n\nSummary:\n{summary}"
         return result, total_tokens
     else:
+        if get_debug():
             print(f"DEBUG: Detected image, analyzing for job {job_id_int}")
 
         # Send progress update for image analysis start
@@ -392,6 +405,7 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
 
         # Check for cancellation before processing image
         if job_id_int and check_job_cancelled(job_id_int):
+            if get_debug():
                 print(f"DEBUG: Job {job_id_int} cancelled before image analysis")
             return "Job cancelled by user", total_tokens
 
@@ -408,6 +422,7 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
 
         result, tokens = analyze_single_image(media_path, full_prompt, model)
         total_tokens += tokens
+        if get_debug():
             print(f"DEBUG: Image analysis completed for job {job_id_int}")
 
         # Send progress update for completion
@@ -436,31 +451,37 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
         return result, total_tokens
 def worker_process(backend_type: str):
     """Main worker process."""
+    if get_debug():
         print(f"DEBUG: Starting Analysis Worker for {backend_type}...")
         print(f"DEBUG: Worker PID: {os.getpid()}")
 
     # Workers use TCP for interprocess communication
     comm = SocketCommunicator(host='127.0.0.1', port=get_backend_worker_port(), comm_type='tcp')
+    if get_debug():
         print(f"DEBUG: Worker connecting to {comm.host}:{comm.port}")
     comm.connect()
+    if get_debug():
         print(f"Analysis Worker connected to backend")
 
     # Register with backend
     register_msg = Message('register', 'register', {'type': f'analysis_{backend_type}'})
     comm.send_message(register_msg)
+    if get_debug():
         print(f"Analysis Worker registered as analysis_{backend_type}")
 
     while True:
         try:
             message = comm.receive_message()
-            if message:
+            if message and get_debug():
                 print(f"DEBUG: Worker {os.getpid()} received message: {message}")
             if message and message.msg_type == 'analyze_request':
+                if get_debug():
                     print(f"DEBUG: Worker received analyze_request: {message.msg_id}")
                 data = message.data
                 media_path = data.get('local_path', data.get('file_name', ''))
                 if not media_path:
                     result = 'No media path provided'
+                    if get_debug():
                         print(f"DEBUG: No media path provided for job {message.msg_id}")
                 else:
                     prompt = data.get('prompt', 'Describe this image.')
@@ -469,8 +490,10 @@ def worker_process(backend_type: str):
                     job_id = message.msg_id  # Use message ID for job identification
                     job_id_int = int(message.msg_id.split('_')[1])  # Extract integer job ID
                     print(f"PROGRESS: Job {job_id_int} accepted - Starting analysis")
+                    if get_debug():
                         print(f"DEBUG: Starting analysis of {media_path} with model {model_path} for job {job_id}")
                     result, tokens_used = analyze_media(media_path, prompt, model_path, interval, job_id_int, comm)
+                    if get_debug():
                         print(f"DEBUG: Analysis completed for job {message.msg_id}, used {tokens_used} tokens")
 
                     # Release model reference (don't unload yet, per requirements)
@@ -478,6 +501,7 @@ def worker_process(backend_type: str):
 
                 # Send result back
                 response = Message('analyze_response', message.msg_id, {'result': result, 'tokens_used': tokens_used})
+                if get_debug():
                     print(f"DEBUG: Sending analyze_response for job {message.msg_id}")
                 comm.send_message(response)
 

vidai/worker_training.py

@@ -27,10 +27,11 @@ import shutil
 import json
 import time
 from .comm import SocketCommunicator, Message
-from .config import get_comm_type, get_backend_worker_port
+from .config import get_comm_type, get_backend_worker_port, get_debug
 
 def train_model(train_path, output_model, description):
     """Perform training."""
+    if get_debug():
         print(f"DEBUG: Starting training with videotrain for output_model {output_model}")
     desc_file = os.path.join(train_path, "description.txt")
     with open(desc_file, "w") as f:
@@ -39,6 +40,7 @@ def train_model(train_path, output_model, description):
     # Assume videotrain is available
     cmd = ["python", "videotrain", train_path, "--output_dir", output_model]
     result = subprocess.run(cmd, capture_output=True, text=True)
+    if get_debug():
         print(f"DEBUG: Training subprocess completed with returncode {result.returncode}")
     if result.returncode == 0:
         return "Training completed!"
@@ -47,6 +49,7 @@ def train_model(train_path, output_model, description):
 
 def worker_process(backend_type: str):
     """Main worker process."""
+    if get_debug():
         print(f"Starting Training Worker for {backend_type}...")
 
     # Workers use TCP for interprocess communication
@@ -60,9 +63,10 @@ def worker_process(backend_type: str):
     while True:
         try:
             message = comm.receive_message()
-            if message:
+            if message and get_debug():
                 print(f"DEBUG: Worker {os.getpid()} received message: {message}")
             if message and message.msg_type == 'train_request':
+                if get_debug():
                     print(f"DEBUG: Worker received train_request: {message.msg_id}")
                 data = message.data
                 output_model = data.get('output_model', './VideoModel')
@@ -71,15 +75,19 @@ def worker_process(backend_type: str):
 
                 if train_dir and os.path.isdir(train_dir):
                     print(f"PROGRESS: Job {message.msg_id} accepted - Starting training")
+                    if get_debug():
                         print(f"DEBUG: Starting training for job {message.msg_id}")
                     result = train_model(train_dir, output_model, description)
                     print(f"PROGRESS: Job {message.msg_id} - 100% - Training completed")
+                    if get_debug():
                         print(f"DEBUG: Training completed for job {message.msg_id}")
                 else:
                     result = "No valid training directory provided"
+                    if get_debug():
                         print(f"DEBUG: No valid training directory for job {message.msg_id}")
 
                 response = Message('train_response', message.msg_id, {'message': result})
+                if get_debug():
                     print(f"DEBUG: Sending train_response for job {message.msg_id}")
                 comm.send_message(response)
             time.sleep(0.1)