Implement config.ini for enhanced persistence of settings (#19)

* Support for coinfig.ini for persistent settings

* feat: Implement dynamic config.ini updates and saves model information and config

* feat: Implement dynamic config.ini updates and saves model information and config

* Remove duplicates and logging

---------

Co-authored-by: ashish <ashish@aesthisia.com>

.gitignore

@@ -2,7 +2,7 @@
 models/models--*
 models/.locks
 models/tmp*
-
+configs/config.ini
 
 #compiled files
 *.pyc

configs/config.ini

@@ -0,0 +1,4 @@
+[Settings]
+execution_provider = 
+repo_id = 
+

core.py

@@ -1,6 +1,8 @@
 import os, shutil
+from configparser import ConfigParser
 
 default_repo_id = "stabilityai/stable-code-instruct-3b"
+config_path = "configs/config.ini"
 default_repo_id_parts = default_repo_id.split("/")
 default_model_folder = f"models--{'--'.join(default_repo_id_parts)}"
 
@@ -22,4 +24,18 @@ def remove_dir(path):
                     shutil.rmtree(model_path)
         print("successfully removed cached models!")
     except OSError as e:
-        print(f"Error: {e.strerror}")
+        print(f"Error: {e.strerror}")
+
+def read_config():
+    config = ConfigParser()
+    config.read(config_path)
+    if config.get('Settings', 'repo_id') == "" and config.get('Settings', 'execution_provider') == "":
+        return None, config
+    else:
+        return config, config
+
+def update_config(config, **kwargs):
+    for key, value in kwargs.items():
+        config.set('Settings', key, value)
+    with open(config_path, 'w') as configfile:
+        config.write(configfile)

webui.py

@@ -7,13 +7,23 @@
 from langchain.llms.base import LLM
 from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, AutoConfig
 
-from core import list_download_models, format_model_name, remove_dir, default_repo_id
+from core import list_download_models, remove_dir, default_repo_id, read_config, update_config
 
 cache_dir = os.path.join(os.getcwd(), "models")
 saved_models_list = list_download_models(cache_dir)
 
 #check if cuda is available
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
+state, config = read_config()
+if state == None: 
+    config.set('Settings', 'execution_provider', device)
+    config.set('Settings', 'repo_id', default_repo_id)
+
+    update_config(config)
+else:
+    default_repo_id = config.get('Settings', 'repo_id')
+    device = config.get('Settings', 'execution_provider')
+
 
 def initialize_model_and_tokenizer(model_name):
     config = AutoConfig.from_pretrained(model_name, cache_dir=cache_dir)
@@ -117,19 +127,24 @@ def removeModelCache():
     def updateExecutionProvider(provider):
         if provider == "cuda":
             if torch.cuda.is_available():
+                device = "cuda"
                 model.cuda()
                 print("Model loaded in cuda", model)
             else:
                 raise gr.Error("Torch not compiled with CUDA enabled. Please make sure cuda is installed.")
 
         else:
+            device = "cpu"
             model.cpu()
 
+        update_config(config, execution_provider=provider)
+
     def loadModel(repo_id):
         global llm_chain, llm
         if repo_id:
             model, tokenizer = initialize_model_and_tokenizer(repo_id)
             llm_chain, llm = init_chain(model, tokenizer)
+            update_config(config, repo_id=repo_id)
             return gr.update(value=repo_id)
         else:
             raise gr.Error("Repo can not be empty!")