Update app.py

app.py

@@ -2,6 +2,7 @@ import os
 import shutil
 import subprocess
 import signal
+
 os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
 import gradio as gr
 
@@ -11,16 +12,12 @@ from huggingface_hub import whoami
 from huggingface_hub import ModelCard
 
 from gradio_huggingfacehub_search import HuggingfaceHubSearch
-
 from apscheduler.schedulers.background import BackgroundScheduler
-
 from textwrap import dedent
 
 HF_TOKEN = os.environ.get("HF_TOKEN")
 OLLAMA_USERNAME = os.environ.get("OLLAMA_USERNAME").lower()
 ollama_pubkey = open("/home/user/.ollama/id_ed25519.pub", "r")
-ollama_q_methods = ["FP16","Q3_K_S", "Q3_K_M", "Q3_K_L", "Q4_0", "Q4_1", "Q4_K_S", "Q4_K_M", "Q5_0", "Q5_1", "Q5_K_S", "Q5_K_M", "Q6_K", "Q8_0"]
-
 
 def process_model(model_id, ollamafy, ollama_q_method, latest, maintainer, oauth_token: gr.OAuthToken | None):
     if oauth_token.token is None:
@@ -30,9 +27,7 @@ def process_model(model_id, ollamafy, ollama_q_method, latest, maintainer, oauth
 
     try:
         api = HfApi(token=oauth_token.token)
-
         dl_pattern = ["*.md", "*.json", "*.model"]
-
         pattern = (
             "*.safetensors"
             if any(
@@ -44,8 +39,8 @@ def process_model(model_id, ollamafy, ollama_q_method, latest, maintainer, oauth
             )
             else "*.bin"
         )
-
         dl_pattern += pattern
+        
         if not os.path.isfile(fp16):
             api.snapshot_download(repo_id=model_id, local_dir=model_name, local_dir_use_symlinks=False, allow_patterns=dl_pattern)
             print("Model downloaded successfully!")
@@ -81,11 +76,13 @@ def process_model(model_id, ollamafy, ollama_q_method, latest, maintainer, oauth
                 ollama_conversion = f"ollama create -f {model_file} {OLLAMA_USERNAME}/{ollama_model_name}:{ollama_q_method.lower()}"
             else:
                 ollama_conversion = f"ollama create -q {ollama_q_method} -f {model_file} {OLLAMA_USERNAME}/{ollama_model_name}:{ollama_q_method.lower()}"
+                
             ollama_conversion_result = subprocess.run(ollama_conversion, shell=True, capture_output=True)
             print(ollama_conversion_result)
             if ollama_conversion_result.returncode != 0:
                 raise Exception(f"Error converting to Ollama: {ollama_conversion_result.stderr}")
-            print("Model converted to Ollama successfully!")
+            else: 
+                print("Model converted to Ollama successfully!")
             
             if maintainer:
                 ollama_push =  f"ollama push  {OLLAMA_USERNAME}/{model_name}:{q_method.lower()}"
@@ -97,15 +94,17 @@ def process_model(model_id, ollamafy, ollama_q_method, latest, maintainer, oauth
             ollama_push_result = subprocess.run(ollama_push, shell=True, capture_output=True)
             print(ollama_push_result)
             if ollama_push_result.returncode != 0:
-                raise Exception(f"Error pushing to Ollama: {ollama_push_result.stderr}")
-            print("Model pushed to Ollama library successfully!")
+                raise Exception(f"Error pushing to Ollama: {ollama_push_result.stderr}")      
+            else: 
+                print("Model pushed to Ollama library successfully!")
             
             ollama_rm_result = subprocess.run(ollama_rm, shell=True, capture_output=True)
             print(ollama_rm_result)
             if ollama_rm_result.returncode != 0:
                 raise Exception(f"Error removing to Ollama: {ollama_rm_result.stderr}")
-            print("Model pushed to Ollama library successfully!")
-       
+            else: 
+                print("Model pushed to Ollama library successfully!")
+          
     
             if latest:
                 ollama_copy =  f"ollama cp  {OLLAMA_USERNAME}/{model_id.lower()}:{q_method.lower()} {OLLAMA_USERNAME}/{model_id.lower()}:latest"
@@ -115,7 +114,7 @@ def process_model(model_id, ollamafy, ollama_q_method, latest, maintainer, oauth
                     raise Exception(f"Error converting to Ollama: {ollama_push_result.stderr}")
                 print("Model pushed to Ollama library successfully!")
                 
-                if maintainer == True:
+                if maintainer:
                     ollama_push_latest =  f"ollama push  {OLLAMA_USERNAME}/{model_name}:latest"
                     ollama_rm_latest =  f"ollama rm  {OLLAMA_USERNAME}/{model_name}:latest"
                 else:    
@@ -159,7 +158,7 @@ with gr.Blocks(css=css) as demo:
     )
 
     ollama_q_method = gr.Dropdown(
-        ollama_q_methods,
+        ["FP16", "Q3_K_S", "Q3_K_M", "Q3_K_L", "Q4_0", "Q4_1", "Q4_K_S", "Q4_K_M", "Q5_0", "Q5_1", "Q5_K_S", "Q5_K_M", "Q6_K", "Q8_0"],
         label="Ollama Lastest Quantization Method",
         info="Chose which quantization will be labled with the latest tag in the Ollama Library",
         value="FP16", 
@@ -176,7 +175,7 @@ with gr.Blocks(css=css) as demo:
     maintainer = gr.Checkbox(
         value=False,
         label="Maintainer",
-        info="This is your original repository on both Hugging Face and Ollama. (DO NOT USE!!!)"
+        info="This is your original repository on both Hugging Face and Ollama. DO NOT USE Unless same USERNAME on both platforms!!!"
     )
     
     iface = gr.Interface(