使用Google Translate API批量翻译关键词时遇随机JSONDecodeError求助

Hey there! That random JSONDecodeError you're hitting is super frustrating—especially since it pops up at different positions every time. Let's break down why this is happening and how to fix it.

What's Causing the Error? #

That error message (Expecting value: line 1 column 1 (char 0)) means the Google Translate API isn't sending back valid JSON when you make a request. This usually happens for a few reasons:

• Network blips: A temporary drop in connection mid-request leads to an empty/incomplete response.
• API rate limiting: Google limits how many requests you can send in a given time. If you're hitting those limits, the API might return an HTML error page instead of JSON.
• Server-side errors: Occasional 500-level errors from Google's servers that don't return valid JSON.

Step-by-Step Fixes #

1. Add Retry Logic for Flaky Requests

When you get a random failure, the easiest fix is to retry the request. You can use the tenacity library to handle this cleanly:
First install it:

pip install tenacity

Then add retry decorators to your translation function:

from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type
import json
import requests
import pandas as pd

@retry(
    stop=stop_after_attempt(3),  # Retry up to 3 times
    wait=wait_exponential(multiplier=1, min=2, max=10),  # Wait longer between retries
    retry=retry_if_exception_type((json.JSONDecodeError, requests.exceptions.RequestException))
)
def translate_text(text, target='en'):
    api_key = "YOUR_API_KEY"
    url = f"https://translation.googleapis.com/language/translate/v2?key={api_key}"
    payload = {
        "q": text,
        "target": target
    }
    response = requests.post(url, json=payload)
    
    # Check if response is successful before parsing
    if response.status_code != 200:
        raise Exception(f"API request failed with status {response.status_code}: {response.text}")
    
    return response.json()['data']['translations'][0]['translatedText']

# Load your Excel file
df = pd.read_excel("your_file.xlsx")

# Apply translation with error handling
df['translated'] = df['keywords'].apply(lambda x: translate_text(x) if pd.notna(x) else x)

# Save the result
df.to_excel("translated_keywords.xlsx", index=False)

2. Slow Down Requests to Avoid Rate Limits

Google's Translate API has rate limits (check your Google Cloud Console for your specific quota). Add a small delay between requests to stay under the limit:

import time

def translate_text(text, target='en'):
    # ... existing code ...
    time.sleep(0.5)  # Wait 0.5 seconds between requests
    # ... rest of the function ...

If you hit a 429 "Too Many Requests" error, you can add logic to wait longer (like 10 seconds) before retrying.

3. Validate Responses Before Parsing

Before trying to decode the JSON, check the response content isn't empty and the status code is 200. This helps you debug what's being returned when things go wrong:

response = requests.post(url, json=payload)
if not response.text:
    raise ValueError("Empty response from API")
if response.status_code != 200:
    print(f"Debug: API returned {response.status_code}, content: {response.text[:500]}")
    raise Exception(f"API request failed")

4. Use Batch Translation to Reduce Requests

Instead of sending one request per keyword, send multiple at once. The API supports translating up to 128 texts per request, which cuts down on the number of requests and reduces the chance of random failures:

def translate_batch(texts, target='en'):
    api_key = "YOUR_API_KEY"
    url = f"https://translation.googleapis.com/language/translate/v2?key={api_key}"
    payload = {
        "q": texts,
        "target": target
    }
    response = requests.post(url, json=payload)
    if response.status_code != 200:
        raise Exception(f"Batch request failed: {response.text}")
    return [item['translatedText'] for item in response.json()['data']['translations']]

# Split your keywords into batches of 100 (stay under API limits)
keywords = df['keywords'].dropna().tolist()
batch_size = 100
translations = []
for i in range(0, len(keywords), batch_size):
    batch = keywords[i:i+batch_size]
    translations.extend(translate_batch(batch))
    time.sleep(1)  # Delay between batches

# Map translations back to the DataFrame
df['translated'] = df['keywords'].map(dict(zip(keywords, translations)))

Final Checks #

• Make sure your Google Cloud account has enough quota for 3000 translations.
• If you're behind a proxy or firewall, ensure it's not blocking the API endpoint (https://translation.googleapis.com).

内容的提问来源于stack exchange，提问作者prasadav