We use essential cookies to make our site work. With your consent, we may also use non-essential cookies to improve user experience and analyze website traffic…

DeepInfra raises $107M Series B to scale the inference cloud — read the announcement

Art That Talks Back: A Hands-On Tutorial on Talking Images
Published on 2025.03.07 by Oguz Vuruskaner
Art That Talks Back: A Hands-On Tutorial on Talking Images

Imagine going to an art gallery where paintings tell their stories. That’s what "Talking Images" do in practice. This tutorial shows you how to make art speak using DeepInfra models. We are going to use:

1-) deepseek-ai/Janus-Pro-7B
2-) hexgrad/Kokoro-82M

Setting Up Environment

First, let’s set up your environment. You’ll need these packages. Here’s the content of requirements.txt:

gradio
requests
python-dotenv
pillow
scipy
numpy
copy

Venv Environment Setup

Show Venv Tutorial

python -m venv venv && (venv\Scripts\activate.bat 2>nul || source venv/bin/activate) && pip install -r requirements.txt
copy

Create .env File

Next, create a .env file in your project folder. Copy your DEEPINFRA_API_TOKEN into it. Your .env file should look like this:

DEEPINFRA_API_TOKEN=your-api-token-here
copy

Replace your-api-token-here with your actual DeepInfra API token.

The Code

Here’s the Python code that makes your images talk. It uses Janus-Pro-7B to describe the image and Kokoro-82M to turn that description into audio.

import os
from io import BytesIO
import gradio as gr
import base64
import requests
from dotenv import load_dotenv, find_dotenv
from scipy.io import wavfile
import numpy as np

_ = load_dotenv(find_dotenv())

def analyze_image(image) -> str:
    url = "https://api.deepinfra.com/v1/inference/deepseek-ai/Janus-Pro-7B"
    headers = {"Authorization": f"bearer {api_token}"}
    buffered = BytesIO()
    if image.mode == "RGBA":
        image = image.convert("RGB")
    format = "JPEG" if image.format == "JPEG" else "PNG"
    image.save(buffered, format=format)
    files = {"image": ("my_image." + format.lower(), buffered.getvalue(), f"image/{format.lower()}")}
    data = {
        "question": "I am this image. You must describe me in my own voice using 'I'. State my colors, shapes, mood, and any notable features with precise detail. Examples: 'I have clouds,' 'I contain sharp lines.' Be vivid, thorough, and factual."
    }
    response = requests.post(url, headers=headers, files=files, data=data)
    return response.json()["response"]

def text_to_speech(text: str) -> tuple:
    url = "https://api.deepinfra.com/v1/inference/hexgrad/Kokoro-82M"
    headers = {
        "Authorization": f"bearer {api_token}",
        "Content-Type": "application/json"
    }
    data = {
        "text": text
    }
    response = requests.post(url, json=data, headers=headers)
    res_json = response.json()
    audio_base64 = res_json["audio"].split(",")[1]
    audio_bytes = base64.b64decode(audio_base64)
    audio_io = BytesIO(audio_bytes)
    sample_rate, audio_data = wavfile.read(audio_io)
    return sample_rate, audio_data

def make_image_talk(image):
    description = analyze_image(image)
    sample_rate, audio_data = text_to_speech(description)
    return sample_rate, audio_data

if __name__ == "__main__":
    api_token = os.environ.get("DEEPINFRA_API_TOKEN")
    interface = gr.Interface(
        fn=make_image_talk,
        inputs=gr.Image(type="pil"),
        outputs=gr.Audio(type="numpy"),
        title="Art That Talks Back",
        description="Upload an image and hear it talk!"
    )
    interface.launch()
copy

Final Look

app.jpg

Try It Yourself!

Ready to hear your own art talk back? Grab yourself an image, run the code, and upload it. Do not forget to follow us on Linkedin and on X.

Related articles
NVIDIA Nemotron API Pricing Guide 2026NVIDIA Nemotron API Pricing Guide 2026<p>While everyone knows Llama 3 and Qwen, a quieter revolution has been happening in NVIDIA&#8217;s labs. They have been taking standard Llama models and &#8220;supercharging&#8221; them using advanced alignment techniques and pruning methods. The result is Nemotron—a family of models that frequently tops the &#8220;Helpfulness&#8221; leaderboards (like Arena Hard), often beating GPT-4o while being significantly [&hellip;]</p>
Deploy Custom LLMs on DeepInfraDeploy Custom LLMs on DeepInfraDid you just finetune your favorite model and are wondering where to run it? Well, we have you covered. Simple API and predictable pricing. Put your model on huggingface Use a private repo, if you wish, we don't mind. Create a hf access token just for the repo for better security. Create c...
Kimi K2 0905 API from Deepinfra: Practical Speed, Predictable Costs, Built for Devs - Deep InfraKimi K2 0905 API from Deepinfra: Practical Speed, Predictable Costs, Built for Devs - Deep Infra<p>Kimi K2 0905 is Moonshot’s long-context Mixture-of-Experts update designed for agentic and coding workflows. With a context window up to ~256K tokens, it can ingest large codebases, multi-file documents, or long conversations and still deliver structured, high-quality outputs. But real-world performance isn’t defined by the model alone—it’s determined by the inference provider that serves it: [&hellip;]</p>