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app.py

@@ -2,6 +2,7 @@ import gradio as gr
 import os
 import json
 import torch
+import subprocess
 from dotenv import load_dotenv
 import logging
 
@@ -38,6 +39,23 @@ MODEL_NAME = model_config.get("model_name_or_path", "unsloth/DeepSeek-R1-Distill
 SPACE_NAME = os.getenv("HF_SPACE_NAME", "phi4training")
 TRAINING_ACTIVE = os.path.exists("TRAINING_ACTIVE")
 
+# Function to start the training process
+def start_training():
+    try:
+        # Create TRAINING_ACTIVE file
+        with open("TRAINING_ACTIVE", "w") as f:
+            f.write("Training in progress")
+            
+        # Run the training script in the background
+        subprocess.Popen(["python", "run_cloud_training.py"], 
+                        stdout=subprocess.PIPE, 
+                        stderr=subprocess.PIPE)
+        
+        return "✅ Training started! Check status below for updates."
+    except Exception as e:
+        logger.error(f"Error starting training: {str(e)}")
+        return f"❌ Error starting training: {str(e)}"
+
 # Create Gradio interface - training status only, no model outputs
 with gr.Blocks(css="footer {visibility: hidden}") as demo:
     gr.Markdown(f"# {SPACE_NAME}: Training Status Dashboard")
@@ -67,8 +85,13 @@ with gr.Blocks(css="footer {visibility: hidden}") as demo:
                 """
             )
     
-    # Add a refresh button to check status
-    refresh_btn = gr.Button("Refresh Status")
+    with gr.Row():
+        # Add buttons for starting training and refreshing status
+        start_btn = gr.Button("Start Training", variant="primary")
+        refresh_btn = gr.Button("Refresh Status")
+        
+    # Output area for training start messages
+    training_output = gr.Markdown("")
     
     def refresh_status():
         # Re-check if training is active
@@ -94,22 +117,23 @@ with gr.Blocks(css="footer {visibility: hidden}") as demo:
         ⚠️ **NOTE**: This space does not provide model outputs during the research training phase.
         """
     
+    # Connect button clicks to functions
+    start_btn.click(start_training, outputs=training_output)
     refresh_btn.click(refresh_status, outputs=status)
     
     gr.Markdown("""
     ### Research Training Information
     This model is being fine-tuned on research-focused datasets and is not available for interactive querying.
     Training logs are available to authorized researchers only.
+    
+    ### Instructions
+    1. Click "Start Training" to begin the fine-tuning process
+    2. Use "Refresh Status" to check training progress
+    3. Training logs are saved to the output directory
     """)
 
 # Launch the interface
 if __name__ == "__main__":
-    # Create an empty TRAINING_ACTIVE file to indicate training is in progress
-    # This would be managed by the actual training script
-    if not os.path.exists("TRAINING_ACTIVE"):
-        with open("TRAINING_ACTIVE", "w") as f:
-            f.write("Training in progress")
-    
     # Start Gradio with minimal features
     logger.info("Starting training status dashboard")
-    demo.launch(share=False) 
+    demo.launch(share=False)  # Removed enable_queue parameter which is no longer supported in Gradio 5.x