Cohere Tutorial: Create 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 Fascinating CoHERE Tutorial

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

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Eager to delve deeper into the captivating world of Cohere AI? Our treasure chest of Cohere tutorials awaits! And 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 we will create a virtual environment for it.

Setup Instructions

Create Cohere and OpenAI accounts.
Download the API Keys for authorization.
Create a .env file and place the keys there.

Install Necessary Libraries

Now let's install all the necessary libraries:

pip install fastapi cohere openai

Create app.py and Start Coding

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

Authorization

Next, we will create a FastAPI app and authorize the Cohere and OpenAI clients:

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

Define Your Prompt

I will define a prompt for Cohere's LLM to predict a dog's breed. This will involve passing examples of dog breeds along with their descriptions:

prompt = "Given a description, predict the dog's breed. Examples:
- A small, energetic dog with a curly coat: Poodle
- A large, loyal dog that loves to fetch: Labrador Retriever"

Create Prediction Endpoint

Now we can define an endpoint for our prediction using Cohere's LLM:

@app.post("/predict")
async def predict(description: str):
    response = cohere_client.generate(prompt=prompt + f'\n{description}\n')
    return response.generations[0].text.strip()

Running the App

Now we can run our app using the command:

uvicorn app:app --reload

We can test our app by sending a request to the endpoint. I will use Postman for that, a popular tool for testing APIs.

Request Details

This is how our request URL should look like:

POST http://localhost:8000/predict

My description looks like:

"A friendly dog known for its love of playing fetch."

Check 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. But the journey doesn't stop here. You can join lablab.ai's AI hackathons to put your new skills to the test in a live setting. It's a fantastic opportunity to learn, share, and innovate alongside a community of like-minded individuals from around the world.

And remember, knowledge is power. Progressing your knowledge in this rapidly growing industry could be the catalyst for a career change. So why wait? Join the AI revolution with lablab.ai and start building with the Cohere API today!