refactor main for utils.py

app.py

@@ -14,28 +14,21 @@ import gradio as gr
 from huggingface_hub import InferenceClient
 import openai
 from openai import OpenAI
-from globalvars import API_BASE, API_KEY, intention_prompt
+from globalvars import API_BASE, intention_prompt, tasks
 from dotenv import load_dotenv
+import re 
+from utils import load_env_variables
+
 
 os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:30'
 os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
 os.environ['CUDA_CACHE_DISABLE'] = '1'
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-def load_env_variables():
-    # Load the .env file
-    load_dotenv()
-
-    # Retrieve the environment variables
-    hf_token = os.getenv('HF_TOKEN')
-    yi_token = os.getenv('YI_TOKEN')
-
-    return hf_token, yi_token
+### Utils
 
 hf_token, yi_token = load_env_variables()
 
-
-
 ## use instruct embeddings
 # Load the tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained('nvidia/NV-Embed-v1', token = hf_token , trust_remote_code=True)

utils.py

@@ -0,0 +1,31 @@
+import re 
+from dotenv import load_dotenv
+import re 
+import os 
+from globalvars import tasks
+
+def load_env_variables():
+    # Load the .env file
+    load_dotenv()
+
+    # Retrieve the environment variables
+    hf_token = os.getenv('HF_TOKEN')
+    yi_token = os.getenv('YI_TOKEN')
+
+    return hf_token, yi_token
+
+def parse_and_route(example_output: str):
+    # Regex pattern to match the true task
+    pattern = r'"(\w+)":\s?true'
+    
+    # Find the true task
+    match = re.search(pattern, example_output)
+    
+    if match:
+        true_task = match.group(1)
+        if true_task in tasks:
+            return {true_task: tasks[true_task]}
+        else:
+            return {true_task: "Task description not found"}
+    else:
+        return "No true task found in the example output"