Update app.py

app.py

@@ -11,7 +11,6 @@ import torch.nn as nn
 from transformers import AutoModel
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import gradio as gr
-import brotli
 
 client = AsyncClient(
     # enable http2
@@ -26,25 +25,17 @@ client = AsyncClient(
 )
 
 def parse_comments(response: Response) -> Dict:
-    log.debug(f"Response status code: {response.status_code}")
-    log.debug(f"Response headers: {response.headers}")
-
     try:
-        if 'content-encoding' in response.headers and response.headers['content-encoding'] == 'br':
-            response_text = brotli.decompress(response.content).decode('utf-8')
-        else:
-            response_text = response.text
-        data = json.loads(response_text)
-    except json.JSONDecodeError as e:
-        log.error(f"Failed to parse JSON response: {e}")
-        log.error(f"Response text: {response_text[:200]}")  # Log only the first 200 characters
+        data = json.loads(response.text)
+    except json.JSONDecodeError:
+        log.error(f"Failed to parse JSON response: {response.text}")
         return {"comments": [], "total_comments": 0}
 
     comments_data = data.get("comments", [])
     total_comments = data.get("total", 0)
 
     if not comments_data:
-        log.warning(f"No comments found in response: {response_text[:200]}")
+        log.warning(f"No comments found in response: {response.text}")
         return {"comments": [], "total_comments": total_comments}
 
     parsed_comments = []
@@ -61,7 +52,7 @@ def parse_comments(response: Response) -> Dict:
 async def scrape_comments(post_id: int, comments_count: int = 20, max_comments: int = None) -> List[Dict]:
     
     def form_api_url(cursor: int):
-        base_url = "https://www.tiktok.com/api/comment/list/?"
+        base_url = "https://www.tiktok.com:8080/api/comment/list/?"
         params = {
             "aweme_id": post_id,
             'count': comments_count,