Update app.py

app.py

@@ -2,19 +2,31 @@ import gradio as gr
 from gradio_huggingfacehub_search import HuggingfaceHubSearch
 import requests
 
-processed_inputs = {}
+"""
+    # OAuth Token Information:
+    # - This is an OAuth token, not a user's password.
+    # - We need the OAuth token to clone the related repository and access its contents.
+    # - As mentioned in the README.md, only read permission is requested, which includes:
+    #   - Read access to your public profile
+    #   - Read access to the content of all your public repos
+    # - The token expires after 60 minutes.
+    # - For more information about OAuth, please refer to the official documentation:
+    #   https://huggingface.co/docs/hub/en/spaces-oauth
+"""
+
 
-def process_inputs(markdown, model_id, q_method, email, oauth_token: gr.OAuthToken | None, profile: gr.OAuthProfile | None):
+processed_inputs = {}
+def process_inputs(model_id, q_method, email, oauth_token: gr.OAuthToken | None, profile: gr.OAuthProfile | None):
     if oauth_token is None or oauth_token.token is None or profile.username is None:
-        return "##### You must be logged in to use this service."
+        return "#### You must be logged in to use this service."
 
     if not model_id or not q_method or not email:
-        return "##### All fields are required!"
+        return "#### All fields are required!"
     
     input_hash = hash((model_id, q_method, oauth_token.token, profile.username))
 
     if input_hash in processed_inputs and processed_inputs[input_hash] == 200:
-        return "##### This request has already been submitted successfully. Please do not submit the same request multiple times."
+        return "#### This request has already been submitted successfully. Please do not submit the same request multiple times."
 
     url = "https://sdk.nexa4ai.com/task"
 
@@ -30,15 +42,14 @@ def process_inputs(markdown, model_id, q_method, email, oauth_token: gr.OAuthTok
     
     if response.status_code == 200:
         processed_inputs[input_hash] = 200  
-        return "##### Your request has been submitted successfully. We will notify you by email once processing is complete. There is no need to submit the same request multiple times."
+        return "#### Your request has been submitted successfully. We will notify you by email once processing is complete. There is no need to submit the same request multiple times."
     else:
         processed_inputs[input_hash] = response.status_code 
-        return f"##### Failed to submit request: {response.text}"
+        return f"#### Failed to submit request: {response.text}"
 
 iface = gr.Interface(
     fn=process_inputs,
     inputs=[
-        gr.Markdown(value="##### 🔔   You must grant access to the model repository before use."),
         HuggingfaceHubSearch(
             label="Hub Model ID",
             placeholder="Search for model id on Huggingface",
@@ -55,17 +66,19 @@ iface = gr.Interface(
     ],
     outputs = gr.Markdown(
         label="output", 
-        value="##### Please enter the model URL, select a quantization method, and provide your email address."
+        value="#### Please enter the model URL, select a quantization method, and provide your email address."
     ),
-    title="Create your own GGUF Quants, blazingly fast ⚡!",
     allow_flagging="never"
 )
 
 theme = gr.themes.Base(text_size="lg")
 with gr.Blocks(theme=theme) as demo:
+    gr.Markdown(value="# 🚀 Unleash the Power of Custom GGML Quantized Models! ⚡")
+    gr.LoginButton(min_width=300)
     gr.Markdown(value="### 🔔   You must be logged in to use this service.")
-    gr.LoginButton(min_width=250)
+    gr.Markdown(value="### 🚨  IMPORTANT: You must grant access to the model repository before use.")
     iface.render()
+    gr.Markdown(value="We sincerely thank our community members, [Perry](https://huggingface.co/PerryCheng614), [Brian](https://huggingface.co/JoyboyBrian), [Qi](https://huggingface.co/qiqiWav), [David](https://huggingface.co/Davidqian123), for their extraordinary contributions to this GGUF converter project.")
 
 demo.launch(share=True)