Create app.py

app.py

@@ -0,0 +1,71 @@
+import gradio as gr
+from gradio_huggingfacehub_search import HuggingfaceHubSearch
+import requests
+
+processed_inputs = {}
+
+def process_inputs(model_id, q_method, email, oauth_token: gr.OAuthToken | None):
+    print(f"model_iddddddd: {model_id}")
+    print(f"q_methodddddd: {q_method}")
+    print(f"emailllllll: {email}")
+    print(f"oauth_tokennnnn: {oauth_token}")
+    if oauth_token is None or oauth_token.token is None:
+        raise ValueError("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, email, oauth_token.token))
+
+    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 = {
+        "model_id": model_id,
+        "q_method": q_method,
+        "email": email,
+        "oauth_token": oauth_token.token
+    }
+    print(f"dataaaaa: {data}")
+    
+    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=[
+        HuggingfaceHubSearch(
+            label="Hub Model ID",
+            placeholder="Search for model id on Huggingface",
+            search_type="model",
+        ),
+        gr.Dropdown(
+            ["Q2_K", "Q3_K_S", "Q3_K_M", "Q3_K_L", "Q4_0", "Q4_K_S", "Q4_K_M", "Q5_0", "Q5_K_S", "Q5_K_M", "Q6_K", "Q8_0"],
+            label="Quantization Method",
+            info="GGML quantisation type",
+            value="Q4_K_M",
+            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()
+with gr.Blocks(theme=theme) as demo:
+    gr.Markdown("You must be logged in to use this service.")
+    gr.LoginButton(min_width=250)
+    iface.render()
+
+demo.launch(share=True)
+