SOLVED: Image Upload with Analysis Response With OpenAI Assistant API v2

API
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(Disclaimer: I am a complete noob at development and coding but this forum has been incredibly helpful…the code for this image upload test was generated by ChatGPT based on successful prompting strategies that I use. this is a general overview of the process that has proven successful for my purposes and I hope it helps you in yours. everyone’s stack setup is different. apply the principals of this model to fit your setup)

After countless hours of troubleshooting and testing with ChatGPT, here’s a practical and scalable way to upload images to Firebase, generate public URLs, and send them to OpenAI for analysis. This solution is designed with current versions of Flask, Firebase, and OpenAI API in mind, ensuring compatibility and performance (effective Nov 2024).

As of the writing date of this post, OpenAI API assistants currently don’t recognize images directly uploaded but the workaround is this: you can upload an image url or via base64 encoded images. In my case below, the image URL is part of the message content sent in a conversation thread (or session). OpenAI’s API interprets this URL as a reference to an external resource (the image), and if instructed, offers a description of the image… provided the model has vision capabilities.https://platform.openai.com/docs/guides/vision

The core workflow involves:

  • Flask backend to receive image uploads and manage the API calls.
  • Firebase Storage to handle and store the images while generating public URLs.
  • Integration with OpenAI’s API to send the image URL and retrieve an analysis.

If you’ve struggled with managing public image URLs, OpenAI payloads, or ensuring end-to-end flow, this guide will help you get a robust solution in place.

Key Steps

1. Set Up Flask Backend

The Flask app serves as the core API for receiving image uploads, sending them to Firebase Storage, and retrieving OpenAI analysis.

Core Functionality:

  • Flask receives the image from a POST request.
  • The image is uploaded to Firebase Storage and a public URL is generated.
  • The public URL is sent to OpenAI’s API for analysis, and the result is returned.

Example Snippet: Here’s how the backend uploads an image to Firebase and generates a public URL:

python

bucket = storage.bucket()
blob = bucket.blob(f"uploads/{str(uuid.uuid4())}_{file.filename}")
blob.upload_from_file(file)
blob.make_public()  # Generate a public URL
public_url = blob.public_url

To analyze the image using OpenAI:

python

response = openai.ChatCompletion.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": f"Analyze this image: {public_url}"}]
)
analysis = response.choices[0].message['content']

2. Frontend Considerations (Local Server Setup)

For those using a React Native or web-based frontend, the goal is to send the image to the Flask backend for processing. You can use FormData to handle the image upload.

React Native Example:

javascript

const sendImageToBackend = async (imageUri) => {
  const formData = new FormData();
  formData.append("file", {
    uri: imageUri,
    name: "image.jpg",
    type: "image/jpeg",
  });

  const response = await fetch("http://127.0.0.1:5000/upload", {
    method: "POST",
    body: formData,
  });

  const result = await response.json();
  console.log(result.analysis);
};

This solution offers a practical approach to test out a problem that many of us have struggled with. It’s clean, scalable, and effective with current versions of the technologies involved. I’m a beginner and this has been a huge challenge for me but thanks to the help of this forum, I found success. Feel free to comment, ask questions or share how you’ve adapted this to your projects!