Hey there, that error you're hitting is a classic JSON decoding issue—it means the translation API is sending back an empty or invalid response instead of the structured JSON data your code expects. This usually pops up due to network blips, API rate limiting, empty input text, or exhausted usage quotas. Let's break down fixes to get your batch translation running smoothly again:

Common Causes & Targeted Fixes #

• Empty or malformed input text: If row['text'] is blank, whitespace-only, or contains unparseable characters, the API might return an empty response. Add a quick check to skip these rows upfront:

  if not row['text'] or row['text'].strip() == "":
      print(f"Skipping empty text at row {index}")
      continue
  

• Rate limiting or quota exhaustion: Most translation APIs (like Google Translate) enforce request rate limits or have usage caps. When you hit these limits, the API often returns empty/garbage responses instead of valid JSON.

  • Add a small delay between requests to avoid triggering rate limits:

    import time
    # After each successful translation
    time.sleep(1)  # Adjust the duration based on your API's rate rules
    

  • Double-check your API provider's dashboard to confirm you haven't used up your monthly/daily quota.

• Temporary network issues: Flaky internet can cause incomplete or empty responses mid-batch. Adding a retry loop will let your code recover from these blips automatically:

  max_retries = 3
  retry_count = 0
  success = False
  while retry_count < max_retries and not success:
      try:
          translated = translator.translate(row['text'], dest='en')
          newrow['translated'] = translated.text
          success = True
      except Exception as e:
          retry_count += 1
          print(f"Retry {retry_count} for row {index} failed: {str(e)}")
          time.sleep(2)  # Longer delay before retrying
  if not success:
      print(f"Failed to translate row {index} after {max_retries} retries")
      continue
  

• Catch specific exceptions: Instead of a broad Exception catch, target the exact errors you're likely facing to log clearer details and handle issues more precisely:

  import json
  # Inside your try block
  except json.JSONDecodeError as e:
      print(f"JSON decode error at row {index}: {str(e)}")
      continue
  except ConnectionError as e:
      print(f"Network error at row {index}: {str(e)}")
      continue
  except Exception as e:
      print(f"Unexpected error at row {index}: {str(e)}")
      continue
  

Modified Full Code #

Putting all these fixes together, your updated code will look like this:

import copy
import time
import json

translatedList = []
for index, row in df.iterrows():
    # Skip empty or whitespace-only text rows
    if not row['text'] or row['text'].strip() == "":
        print(f"Skipping empty text at row {index}")
        continue
    
    newrow = copy.deepcopy(row)
    max_retries = 3
    retry_count = 0
    success = False
    
    while retry_count < max_retries and not success:
        try:
            # Translate 'text' column
            translated = translator.translate(row['text'], dest='en')
            newrow['translated'] = translated.text
            success = True
        except json.JSONDecodeError as e:
            retry_count += 1
            print(f"Retry {retry_count} for row {index} (JSON error): {str(e)}")
            time.sleep(2)
        except ConnectionError as e:
            retry_count += 1
            print(f"Retry {retry_count} for row {index} (Network error): {str(e)}")
            time.sleep(2)
        except Exception as e:
            retry_count += 1
            print(f"Retry {retry_count} for row {index} (Unexpected error): {str(e)}")
            time.sleep(2)
    
    if success:
        translatedList.append(newrow)
        # Add delay to avoid hitting rate limits
        time.sleep(1)
    else:
        print(f"Failed to process row {index} after {max_retries} attempts")

Give these changes a test run—they should help you bypass the empty response errors and make your batch translation workflow much more robust.

内容的提问来源于stack exchange，提问作者Kerem