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app.py

@@ -22,8 +22,13 @@ logger = logging.getLogger(__name__)
 # Load environment variables
 load_dotenv()
 
+# Get script directory - important for Hugging Face Space paths
+SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+BASE_DIR = os.path.abspath(os.path.join(SCRIPT_DIR, "."))
+
 # Load config file
 def load_config(config_path="transformers_config.json"):
+    config_path = os.path.join(BASE_DIR, config_path)
     try:
         with open(config_path, 'r') as f:
             config = json.load(f)
@@ -43,8 +48,22 @@ SPACE_NAME = os.getenv("HF_SPACE_NAME", "phi4training")
 # Function to run training in a thread and stream output to container logs
 def run_training():
     """Run the training script and stream its output to container logs"""
+    # Locate training script using absolute path
+    training_script = os.path.join(BASE_DIR, "run_cloud_training.py")
+    
+    # Check if file exists and log the path
+    if not os.path.exists(training_script):
+        print(f"ERROR: Training script not found at: {training_script}")
+        print(f"Current directory: {os.getcwd()}")
+        print("Available files:")
+        for file in os.listdir(BASE_DIR):
+            print(f" - {file}")
+        return
+    
+    print(f"Found training script at: {training_script}")
+    
     process = subprocess.Popen(
-        ["python", "run_cloud_training.py"],
+        [sys.executable, training_script],
         stdout=subprocess.PIPE,
         stderr=subprocess.STDOUT,
         universal_newlines=True,
@@ -62,6 +81,8 @@ def start_training():
         # Print directly to container logs
         print("\n===== STARTING TRAINING PROCESS =====\n")
         print(f"Model: {MODEL_NAME}")
+        print(f"Base directory: {BASE_DIR}")
+        print(f"Current working directory: {os.getcwd()}")
         print(f"Training with configuration from transformers_config.json")
         print("Training logs will appear below:")
         print("=" * 50)
@@ -147,7 +168,12 @@ with gr.Blocks(css="footer {visibility: hidden}") as demo:
 if __name__ == "__main__":
     # Start Gradio with minimal features
     print("\n===== RESEARCH TRAINING DASHBOARD STARTED =====\n")
-    print("Click 'Start Training' to begin the fine-tuning process")
+    print(f"Base directory: {BASE_DIR}")
+    print(f"Current working directory: {os.getcwd()}")
+    print("Available files:")
+    for file in os.listdir(BASE_DIR):
+        print(f" - {file}")
+    print("\nClick 'Start Training' to begin the fine-tuning process")
     print("All training output will appear in these logs")
     logger.info("Starting research training dashboard")
     demo.launch(share=False)