Separate different scans using uid

app_text_classification.py

@@ -102,8 +102,7 @@ def get_demo(demo):
         )
 
     with gr.Row():
-        logs = gr.Textbox(label="Giskard Bot Evaluation Log:", visible=False)
-        demo.load(get_logs_file, None, logs, every=0.5)
+        logs = gr.Textbox(value=get_logs_file, label="Giskard Bot Evaluation Log:", visible=False, every=0.5)
 
     dataset_id_input.change(
         check_dataset_and_get_config,

io_utils.py

@@ -1,4 +1,5 @@
 import os
+from pathlib import Path
 import subprocess
 
 import yaml
@@ -6,6 +7,7 @@ import yaml
 import pipe
 
 YAML_PATH = "./cicd/configs"
+LOG_FILE = "temp_log"
 
 
 class Dumper(yaml.Dumper):
@@ -28,7 +30,6 @@ def read_scanners(uid):
     with open(get_yaml_path(uid), "r") as f:
         config = yaml.load(f, Loader=yaml.FullLoader)
         scanners = config.get("detectors", [])
-    f.close()
     return scanners
 
 
@@ -38,11 +39,9 @@ def write_scanners(scanners, uid):
         config = yaml.load(f, Loader=yaml.FullLoader)
         if config:
             config["detectors"] = scanners
-    f.close()
     # save scanners to detectors in yaml
     with open(get_yaml_path(uid), "w") as f:
         yaml.dump(config, f, Dumper=Dumper)
-    f.close()
 
 
 # read model_type from yaml file
@@ -51,7 +50,6 @@ def read_inference_type(uid):
     with open(get_yaml_path(uid), "r") as f:
         config = yaml.load(f, Loader=yaml.FullLoader)
         inference_type = config.get("inference_type", "")
-    f.close()
     return inference_type
 
 
@@ -66,11 +64,9 @@ def write_inference_type(use_inference, inference_token, uid):
             config["inference_type"] = "hf_pipeline"
             # FIXME: A quick and temp fix for missing token
             config["inference_token"] = ""
-    f.close()
     # save inference_type to inference_type in yaml
     with open(get_yaml_path(uid), "w") as f:
         yaml.dump(config, f, Dumper=Dumper)
-    f.close()
 
 
 # read column mapping from yaml file
@@ -113,21 +109,20 @@ def convert_column_mapping_to_json(df, label=""):
 
 def get_logs_file():
     try:
-        file = open(f"./tmp/temp_log", "r")
-        return file.read()
+        with open(LOG_FILE, "r") as file:
+            return file.read()
     except Exception:
         return "Log file does not exist"
 
 
-def write_log_to_user_file(id, log):
-    with open(f"./tmp/temp_log", "a") as f:
+def write_log_to_user_file(task_id, log):
+    with open(f"./tmp/{task_id}.log", "a") as f:
         f.write(log)
-    f.close()
 
 
-def save_job_to_pipe(id, job, description, lock):
+def save_job_to_pipe(task_id, job, description, lock):
     with lock:
-        pipe.jobs.append((id, job, description))
+        pipe.jobs.append((task_id, job, description))
 
 
 def pop_job_from_pipe():
@@ -135,14 +130,17 @@ def pop_job_from_pipe():
         return
     job_info = pipe.jobs.pop()
     pipe.current = job_info[2]
-    write_log_to_user_file(job_info[0], f"Running job id {job_info[0]}\n")
+    task_id = job_info[0]
+    write_log_to_user_file(task_id, f"Running job id {task_id}\n")
     command = job_info[1]
 
-    log_file = open(f"./tmp/temp_log", "a")
-    p = subprocess.Popen(
-        command,
-        stdout=log_file,
-        stderr=log_file,
-    )
-    p.wait()
+    # Link to LOG_FILE
+    log_file_path = Path(LOG_FILE)
+    if log_file_path.exists():
+        log_file_path.unlink()
+    os.symlink(f"./tmp/{task_id}.log", LOG_FILE)
+
+    with open(f"./tmp/{task_id}.log", "a") as log_file:
+        p = subprocess.Popen(command, stdout=log_file, stderr=subprocess.STDOUT)
+        p.wait()
     pipe.current = None