Cohere Tutorial: Build a Dog Breed Recognition API

Why Build an AI App Using Cohere API?

In today's prodigious sea of information, discerning an animal or its breed from a mere description can be a Herculean task. Embrace the magic of Cohere AI—a groundbreaking tool poised to revolutionize data interactions.

Embark on a CoHERE Tutorial

Join us on a fascinating quest to forge an API that pinpoints a dog's breed, fueled by a brief description. Powered by **Cohere API** and visuals generated by **DALLE 2**, this venture showcases the true potential of AI in crafting a Cohere app.

Engage in the Cohere Community

Eager to delve deeper into the captivating world of Cohere AI? Access our treasure chest of Cohere tutorials! For those yearning for communal learning, our remarkable AI Hackathons unite global enthusiasts to experiment with this and other tutorials. Seize the opportunity to learn, collaborate, and innovate like never before!

Let’s Start

We need to start by creating a directory for our project. We will call it dog-breed-recognition and then create a virtual environment for it. Ensure you create a Cohere and OpenAI account, and download the API Keys for authorization. Let’s create a .env file to securely place these keys:

Install Necessary Libraries

Now let’s install all the necessary libraries to build our application:

pip install fastapi cohere openai

Creating an app.py File

Next, we can create an app.py file and start writing our code. First, we need to import all the necessary libraries and load environment variables:

import os
from fastapi import FastAPI
import cohere
import openai

# Load environment variables
from dotenv import load_dotenv
load_dotenv()

Authorization

Now we need to create a FastAPI app and authorize Cohere and OpenAI clients:

app = FastAPI()
cohere_client = cohere.Client(os.getenv('COHERE_API_KEY'))
openai.api_key = os.getenv('OPENAI_API_KEY')

Defining the Prediction Prompt

Let’s define a prompt for Cohere's LLM (Large Language Model). It will be used to generate a prediction of a dog's breed. Prepare a structure for our description, which we'll pass to the model:

prompt = "Based on the following description, predict the dog's breed:"
# examples of dog's breeds and their descriptions would be defined here

Setting Up an Endpoint

Now we can define an endpoint for our prediction. We will use Cohere's LLM to generate a prediction and return the result:

@app.post('/predict')
def predict(description: str):
    result = cohere_client.generate(prompt=prompt + description)
    return result.generations[0].text

Running the App

Now we can run our app using the command:

uvicorn app:app --reload

Testing the App

To test our app, we can send a request to the endpoint using Postman. Here’s how the request URL should look like:

http://127.0.0.1:8000/predict

My description looks like:

"A friendly dog with curly hair and a playful demeanor."

Check the Results

The results speak for themselves! It's truly remarkable how much insight we can extract from text today. We encourage you to try your own descriptions and share your results on our lablab.ai Discord!

Conclusion

In this Cohere tutorial, we've shown you how to build a Cohere app, specifically a dog breed recognition API. The journey doesn't stop here. Join lablab.ai's AI hackathons to put your new skills to the test in a live setting. This is a fantastic opportunity to learn, share, and innovate alongside a community of like-minded individuals around the globe.

Remember, knowledge is power. Advancing your expertise in this rapidly evolving industry could catalyze a career change. So why wait? Join the AI revolution with lablab.ai and start building with the Cohere API today!