Optimizing Gemini Pro Vision for Real-Time Image Analysis

Google AI Studio
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I’m developing a web application that needs to process multiple product images in real-time using Gemini Pro Vision. What’s the most efficient way to structure API calls to handle:

  1. Batch processing of 50+ images per minute
  2. Response time optimization
  3. Best practices for rate limiting
  4. Error handling strategies

I’ve already implemented basic image analysis, but need to scale it for production use. Would love to hear from those who’ve successfully deployed similar solutions!

3

UPDATE - I found an efficient approach to handle your Gemini Pro Vision image processing requirements:

For batch processing and optimization, implement a queue-based system with worker pools:

from google.cloud import aiplatform
from concurrent.futures import ThreadPoolExecutor
import asyncio
from ratelimit import limits, sleep_and_retry

@sleep_and_retry
@limits(calls=60, period=60)  # Rate limit: 60 calls per minute
async def process_single_image(image_data):
    try:
        model = aiplatform.GenerativeModel('gemini-pro-vision')
        response = await model.generate_content(image_data)
        return response
    except Exception as e:
        return {'error': str(e), 'image': image_data}

async def batch_processor(image_queue):
    with ThreadPoolExecutor(max_workers=10) as executor:
        tasks = []
        while not image_queue.empty():
            image = await image_queue.get()
            task = asyncio.create_task(process_single_image(image))
            tasks.append(task)
        results = await asyncio.gather(*tasks, return_exceptions=True)
    return results

This structure allows me to efficiently process large batches while maintaining API limits and handling errors gracefully.