init

README.md

@@ -1,13 +1,16 @@
 ---
-title: Gguf Convertor
-emoji: 🐠
-colorFrom: red
-colorTo: purple
+title: Nexa AI GGUF Convertor
+emoji: 😻
+colorFrom: green
+colorTo: gray
 sdk: gradio
-sdk_version: 4.29.0
+sdk_version: 4.28.3
 app_file: app.py
 pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+hf_oauth: true
+hf_oauth_scopes:
+ - read-repos
+ - write-repos
+ - manage-repos
+ - inference-api
+---

app.py

@@ -0,0 +1,71 @@
+import gradio as gr
+from gradio_huggingfacehub_search import HuggingfaceHubSearch
+import requests
+
+processed_inputs = {}
+
+def process_inputs(markdown, 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."
+
+    if not model_id or not q_method or not email:
+        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."
+
+    url = "https://sdk.nexa4ai.com/task"
+
+    data = {
+        "repository_url": f"https://huggingface.co/{model_id}",
+        "username": profile.username,
+        "access_token": oauth_token.token,
+        "email": email,
+        "quantization_option": q_method,
+    }
+    
+    response = requests.post(url, json=data)
+    
+    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."
+    else:
+        processed_inputs[input_hash] = response.status_code 
+        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",
+            search_type="model",
+        ),
+        gr.Dropdown(
+            ["q2_K", "q3_K", "q3_K_S", "q3_K_M", "q3_K_L", "q4_0", "q4_1", "q4_K", "q4_K_S", "q4_K_M", "q5_0", "q5_1", "q5_K", "q5_K_S", "q5_K_M", "q6_K", "q8_0", "f16"],
+            label="Quantization Option",
+            info="GGML quantisation options",
+            value="q4_0",
+            filterable=False
+        ),
+        gr.Textbox(label="Email", placeholder="Enter your email here")
+    ],
+    outputs = gr.Markdown(
+        label="output", 
+        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="### 🔔   You must be logged in to use this service.")
+    gr.LoginButton(min_width=250)
+    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), for their extraordinary contributions to this GGUF converter project.")
+
+demo.launch(share=True)

requirements.txt

@@ -0,0 +1,2 @@
+gradio
+gradio_huggingfacehub_search