Fix long queue waits with mechanism to prevent running duplicate jobs

app.py

@@ -1,5 +1,6 @@
 import os
 import pathlib
+import hashlib
 import random
 import string
 import tempfile
@@ -113,12 +114,40 @@ examples = [[str(f)] for f in pathlib.Path("examples").glob("*.yaml")]
 COMMUNITY_HF_TOKEN = os.getenv("COMMUNITY_HF_TOKEN")
 
 
+# A dictionary to store active jobs and their respective job IDs (which will be used to track them)
+active_jobs = {}
+
+def get_yaml_hash(yaml_config: str) -> str:
+    """Generate a hash for the YAML config to detect duplicates."""
+    return hashlib.sha256(yaml_config.encode("utf-8")).hexdigest()
+
 def merge(yaml_config: str, hf_token: str, repo_name: str) -> Iterable[List[Log]]:
     runner = LogsViewRunner()
 
     if not yaml_config:
         yield runner.log("Empty yaml, pick an example below", level="ERROR")
         return
+
+    yaml_hash = get_yaml_hash(yaml_config)
+    
+    # Check if this YAML job is already running
+    if yaml_hash in active_jobs:
+        old_job_id = active_jobs[yaml_hash]
+        yield runner.log(f"Duplicate job detected! An identical job is already running with Job ID: {old_job_id}.", level="WARNING")
+        user_input = yield gradio.inputs.Button(label="Continue with new job", info="Do you want to cancel the old job and continue with the new one?")
+        
+        if user_input == "Continue with new job":
+            # Cancel the old job and remove it from active jobs
+            runner.log(f"Cancelling the old job with Job ID: {old_job_id}")
+            # This part assumes you have the ability to cancel the previous job if needed
+            # In real implementation, you'd stop the old task/process here
+            active_jobs.pop(yaml_hash)  # Remove the old job from the active jobs list
+        else:
+            # If user chooses not to continue, exit
+            yield runner.log("Duplicate job detected. Operation aborted.", level="ERROR")
+            return
+    
+    # Proceed with the merge
     try:
         merge_config = MergeConfiguration.model_validate(yaml.safe_load(yaml_config))
     except Exception as e:
@@ -170,7 +199,7 @@ def merge(yaml_config: str, hf_token: str, repo_name: str) -> Iterable[List[Log]
             return
 
         # Set tmp HF_HOME to avoid filling up disk Space
-        tmp_env = os.environ.copy()  # taken from https://stackoverflow.com/a/4453495
+        tmp_env = os.environ.copy() # taken from https://stackoverflow.com/a/4453495
         tmp_env["HF_HOME"] = f"{tmpdirname}/.cache"
         full_cli = cli + f" --lora-merge-cache {tmpdirname}/.lora_cache"
         yield from runner.run_command(full_cli.split(), cwd=merged_path, env=tmp_env)
@@ -188,6 +217,9 @@ def merge(yaml_config: str, hf_token: str, repo_name: str) -> Iterable[List[Log]
         )
         yield runner.log(f"Model successfully uploaded to HF: {repo_url.repo_id}")
 
+    # Track this YAML as an active job
+    active_jobs[yaml_hash] = "new_job_id"
+
 # This is workaround. As the space always getting stuck.
 def _restart_space():
     huggingface_hub.HfApi().restart_space(repo_id="arcee-ai/mergekit-gui", token=COMMUNITY_HF_TOKEN, factory_reboot=False)