Farming Responsibly Through Limitation A gpt-based predictor to evaluate how much fertilizer needs to go on a piece of land, to point out overfertilization
To integrate the GPT-4 API and FRTL's 2 servers follow these steps:
Make an HTML page based on the FRTL.ai website to host
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Fertilizer Recommendation</title>
<link rel="stylesheet" href="styles.css">
</head>
<body>
<h1>Fertilizer Recommendation for Farmers</h1>
<form id="fertilizer-form">
<label for="crop-type">Crop Type:</label>
<input type="text" id="crop-type" name="crop-type" required>
<label for="soil-type">Soil Type:</label>
<input type="text" id="soil-type" name="soil-type" required>
<label for="location">Location:</label>
<input type="text" id="location" name="location" required>
<button type="submit">Get Recommendation</button>
</form>
<div id="result"></div>
<script src="script.js"></script>
</body>
</html>
- Connect to the GPT-4 API: To interact with the GPT-4 API, you'll need to make a POST request from your JavaScript code. To ensure security and avoid exposing your API key on the client-side, you should create a backend server to handle the API request. You can use Node.js with Express, for example.
Install the required packages for your backend server:
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npm init
npm install express body-parser axios cors
Create a file named server.js and add the following code:
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const express = require('express');
const bodyParser = require('body-parser');
const axios = require('axios');
const cors = require('cors');
const app = express();
app.use(bodyParser.json());
app.use(cors());
const API_KEY = 'your_openai_api_key';
const API_URL = 'https://api.openai.com/v1/engines/davinci-codex/completions';
app.post('/api/gpt-4', async (req, res) => {
const { cropType, soilType, location } = req.body;
const prompt = `How much fertilizer should be applied for crop "${cropType}" in soil type "${soilType}" and location "${location}" to prevent overfertilization?`;
try {
const response = await axios.post(API_URL, {
prompt,
max_tokens: 50,
n: 1,
stop: null,
temperature: 0.5,
}, {
headers: {
'Authorization': `Bearer ${API_KEY}`,
'Content-Type': 'application/json',
}
});
const recommendation = response.data.choices[0].text.trim();
res.json({ recommendation });
} catch (error) {
res.status(500).json({ error: 'Error retrieving data from GPT-4 API' });
}
});
const PORT = process
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do this in julia instead of java, using as much julia as possible
To implement the same functionality in Julia, you can use the HTTP.jl and JSON3.jl packages. First, you'll need to install them:
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julia -e 'using Pkg; Pkg.add(["HTTP", "JSON3"])'
Next, create a file named server.jl and add the following code:
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using HTTP
using JSON3
const API_KEY = "your_openai_api_key"
const API_URL = "https://api.openai.com/v1/engines/davinci-codex/completions"
function fetch_gpt4_recommendation(cropType, soilType, location)
prompt = "How much fertilizer should be applied for crop \"$(cropType)\" in soil type \"$(soilType)\" and location \"$(location)\" to prevent overfertilization?"
headers = [
"Authorization" => "Bearer $(API_KEY)",
"Content-Type" => "application/json"
]
body = JSON3.write(Dict(
"prompt" => prompt,
"max_tokens" => 50,
"n" => 1,
"stop" => nothing,
"temperature" => 0.5
))
response = HTTP.post(API_URL, headers, body)
parsed_response = JSON3.read(response.body, Dict)
recommendation = strip(parsed_response["choices"][1]["text"])
return recommendation
end
router = HTTP.Router()
HTTP.@register(router, "POST", "/api/gpt-4") do request::HTTP.Request
request_data = JSON3.read(IOBuffer(request.body), Dict)
cropType = request_data["cropType"]
soilType = request_data["soilType"]
location = request_data["location"]
try
recommendation = fetch_gpt4_recommendation(cropType, soilType, location)
return HTTP.Response(200, JSON3.write(Dict("recommendation" => recommendation)))
catch e
return HTTP.Response(500, JSON3.write(Dict("error" => "Error retrieving data from GPT-4 API")))
end
end
server = HTTP.Server(router)
HTTP.serve(server, "0.0.0.0", 8000)
To run the server, execute the following command:
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julia server.jl
Now, the server will be listening for requests at http://localhost:8000. You can adapt the previous HTML and JavaScript code to make a request to this new Julia server. Change the fetch function call in the script.js file:
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fetch('/api/gpt-4', {
method: 'POST',
headers: {
'Content-Type': 'application/json'
},
body: JSON.stringify({ cropType, soilType, location })
})
By following these steps, you will have implemented a simple website that connects to a GPT-4 API using a Julia backend server to prevent overfertilization for specific crops.