app.py: fix formatting issues, move gguf before gptq and update repo_id

app.py

@@ -15,18 +15,18 @@ api = HfApi()
 fs = HfFileSystem()
 
 def restart_space():
-  time.sleep(36000)
-  api.restart_space(repo_id="Weyaxi/thebloke-quantized-models", token=HF_TOKEN)
+    time.sleep(36000)
+    api.restart_space(repo_id="Tanvir1337/thebloke-quantized-models", token=HF_TOKEN)
 
 text = f"""
 🎯 The Leaderboard aims to track TheBloke's quantized models.
 
 ## 📚 Type Of Models
 
-- GPTQ
-
 - GGUF
 
+- GPTQ
+
 - AWQ
 
 - GGML
@@ -101,32 +101,32 @@ for model, values in liste.items():
             ggml_value = value
 
     authors.append(model.split('/')[0])
-    gptq.append(gptq_value)
     gguf.append(gguf_value)
+    gptq.append(gptq_value)
     awq.append(awq_value)
     ggml.append(ggml_value)
 
 
-df = pd.DataFrame({'👤 Author Name': authors, '🤖 Model Name': models, '👍 GPTQ': gptq, '📥 GGUF': gguf, '🤷‍♂️ AWQ': awq, '😭 GGML': ggml})
+df = pd.DataFrame({'👤 Author Name': authors, '🤖 Model Name': models, '📥 GGUF': gguf, '👍 GPTQ': gptq, '🤷‍♂️ AWQ': awq, '😭 GGML': ggml})
 
 
 def search(search_text):
-  if not search_text:
-    return df
+    if not search_text:
+        return df
 
-  if len(search_text.split('/'))>1:
-    return df[df['🤖 Model Name'] == clickable(search_text)]
-  else:
-    return df[df['👤 Author Name'] == clickable(search_text)]
+    if len(search_text.split('/'))>1:
+        return df[df['🤖 Model Name'] == clickable(search_text)]
+    else:
+        return df[df['👤 Author Name'] == clickable(search_text)]
 
 
 def clickable(x):
-  return None if not x else f'<a target="_blank" href="https://huggingface.co/{x}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{x}</a>'
+    return None if not x else f'<a target="_blank" href="https://huggingface.co/{x}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{x}</a>'
 
 
 def to_clickable(df):
     for column in list(df.columns):
-          df[column] = df[column].apply(lambda x: clickable(x))
+        df[column] = df[column].apply(lambda x: clickable(x))
     return df
 
 
@@ -145,4 +145,4 @@ with gr.Blocks() as demo:
     search_bar.submit(fn=search, inputs=search_bar, outputs=gr_df)
 
 threading.Thread(target=restart_space).start()
-demo.launch()
+demo.launch()