Learning FLask: Stuck at OpenAI api

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<code>from flask import Flask, request, jsonify, send_from_directory
import openai
from openai import OpenAI
import os
app = Flask(__name__)
openai.api_key = 'sk-' # I added my key here.
@app.route('/')
def index():
return send_from_directory('.', 'index.html')
@app.route('/analyze', methods=['POST'])
def analyze_cv():
if 'cv' not in request.files or 'job_description' not in request.files:
return jsonify({'error': 'CV and Job Description files are required'}), 400
cv = request.files['cv'].read()
job_description = request.files['job_description'].read()
try:
cv = cv.decode('utf-8')
except UnicodeDecodeError:
cv = cv.decode('latin-1')
try:
job_description = job_description.decode('utf-8')
except UnicodeDecodeError:
job_description = job_description.decode('latin-1')
prompt = f"Given the following job description:n{job_description}nnAssess the suitability of the candidate based on the CV:n{cv}nnScore the candidate from 1 to 10 and provide an explanation."
try:
client = OpenAI(api_key = os.environ.get("OPENAI_API_KEY"),)
openai.Completion.create
response = client.Completions.create(
engine="text-davinci-003",
prompt=prompt,
temperature=0.7,
max_tokens=1024
)
score = int(response.choices[0].text.split()[0])
explanation = response.choices[0].text.split('n')[1]
return jsonify({'score': score, 'explanation': explanation}), 200
except Exception as e:
print(e)
return jsonify({'error': 'An error occurred while analyzing the CV'}), 500
if __name__ == '__main__':
app.run(debug=True)
</code>
<code>from flask import Flask, request, jsonify, send_from_directory import openai from openai import OpenAI import os app = Flask(__name__) openai.api_key = 'sk-' # I added my key here. @app.route('/') def index(): return send_from_directory('.', 'index.html') @app.route('/analyze', methods=['POST']) def analyze_cv(): if 'cv' not in request.files or 'job_description' not in request.files: return jsonify({'error': 'CV and Job Description files are required'}), 400 cv = request.files['cv'].read() job_description = request.files['job_description'].read() try: cv = cv.decode('utf-8') except UnicodeDecodeError: cv = cv.decode('latin-1') try: job_description = job_description.decode('utf-8') except UnicodeDecodeError: job_description = job_description.decode('latin-1') prompt = f"Given the following job description:n{job_description}nnAssess the suitability of the candidate based on the CV:n{cv}nnScore the candidate from 1 to 10 and provide an explanation." try: client = OpenAI(api_key = os.environ.get("OPENAI_API_KEY"),) openai.Completion.create response = client.Completions.create( engine="text-davinci-003", prompt=prompt, temperature=0.7, max_tokens=1024 ) score = int(response.choices[0].text.split()[0]) explanation = response.choices[0].text.split('n')[1] return jsonify({'score': score, 'explanation': explanation}), 200 except Exception as e: print(e) return jsonify({'error': 'An error occurred while analyzing the CV'}), 500 if __name__ == '__main__': app.run(debug=True) </code>
from flask import Flask, request, jsonify, send_from_directory
import openai
from openai import OpenAI
import os

app = Flask(__name__)


openai.api_key = 'sk-' # I added my key here.

@app.route('/')
def index():
    return send_from_directory('.', 'index.html')

@app.route('/analyze', methods=['POST'])
def analyze_cv():
    if 'cv' not in request.files or 'job_description' not in request.files:
        return jsonify({'error': 'CV and Job Description files are required'}), 400

    cv = request.files['cv'].read()
    job_description = request.files['job_description'].read()

    try:
        cv = cv.decode('utf-8')
    except UnicodeDecodeError:
        cv = cv.decode('latin-1')

    try:
        job_description = job_description.decode('utf-8')
    except UnicodeDecodeError:
        job_description = job_description.decode('latin-1')


    prompt = f"Given the following job description:n{job_description}nnAssess the suitability of the candidate based on the CV:n{cv}nnScore the candidate from 1 to 10 and provide an explanation."


    try:
        client = OpenAI(api_key = os.environ.get("OPENAI_API_KEY"),)
        openai.Completion.create
        response = client.Completions.create(
            engine="text-davinci-003",
            prompt=prompt,
            temperature=0.7,
            max_tokens=1024
        )

        score = int(response.choices[0].text.split()[0])
        explanation = response.choices[0].text.split('n')[1]

        return jsonify({'score': score, 'explanation': explanation}), 200
    
    except Exception as e:
        print(e)  
        return jsonify({'error': 'An error occurred while analyzing the CV'}), 500

if __name__ == '__main__':
    app.run(debug=True)
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<code><!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>CV Analyzer</title>
</head>
<body>
<h1>CV Analyzer</h1>
<input type="file" id="cv" accept=".pdf,.doc,.docx">
<input type="file" id="job_description" accept=".pdf,.txt,.doc,.docx">
<button onclick="analyze()">Analyze</button>
<div id="result"></div>
<script>
function analyze() {
var cvFile = document.getElementById('cv').files[0];
var jobDescriptionFile = document.getElementById('job_description').files[0];
if (!cvFile || !jobDescriptionFile) {
alert('Please upload both CV and Job Description files.');
return;
}
var formData = new FormData();
formData.append('cv', cvFile);
formData.append('job_description', jobDescriptionFile);
fetch('/analyze', {
method: 'POST',
body: formData
})
.then(response => response.json())
.then(data => {
document.getElementById('result').innerHTML = `
<h2>Score: ${data.score}</h2>
<p>${data.explanation}</p>
`;
})
.catch(error => console.error('Error:', error));
}
</script>
</body>
</html>
</code>
<code><!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>CV Analyzer</title> </head> <body> <h1>CV Analyzer</h1> <input type="file" id="cv" accept=".pdf,.doc,.docx"> <input type="file" id="job_description" accept=".pdf,.txt,.doc,.docx"> <button onclick="analyze()">Analyze</button> <div id="result"></div> <script> function analyze() { var cvFile = document.getElementById('cv').files[0]; var jobDescriptionFile = document.getElementById('job_description').files[0]; if (!cvFile || !jobDescriptionFile) { alert('Please upload both CV and Job Description files.'); return; } var formData = new FormData(); formData.append('cv', cvFile); formData.append('job_description', jobDescriptionFile); fetch('/analyze', { method: 'POST', body: formData }) .then(response => response.json()) .then(data => { document.getElementById('result').innerHTML = ` <h2>Score: ${data.score}</h2> <p>${data.explanation}</p> `; }) .catch(error => console.error('Error:', error)); } </script> </body> </html> </code>
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>CV Analyzer</title>
</head>
<body>
    <h1>CV Analyzer</h1>
    <input type="file" id="cv" accept=".pdf,.doc,.docx">
    <input type="file" id="job_description" accept=".pdf,.txt,.doc,.docx">
    <button onclick="analyze()">Analyze</button>
    <div id="result"></div>

    <script>
        function analyze() {
            var cvFile = document.getElementById('cv').files[0];
            var jobDescriptionFile = document.getElementById('job_description').files[0];

            if (!cvFile || !jobDescriptionFile) {
                alert('Please upload both CV and Job Description files.');
                return;
            }

            var formData = new FormData();
            formData.append('cv', cvFile);
            formData.append('job_description', jobDescriptionFile);

            fetch('/analyze', {
                method: 'POST',
                body: formData
            })
            .then(response => response.json())
            .then(data => {
                document.getElementById('result').innerHTML = `
                    <h2>Score: ${data.score}</h2>
                    <p>${data.explanation}</p>
                `;
            })
            .catch(error => console.error('Error:', error));
        }
    </script>
</body>
</html>

So I coded the above two file. But I am unable to get the “Score”.
On console this error pops up again and again:

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<code>The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_API_KEY environment variable
127.0.0.1 - - [04/May/2024 20:55:38] "POST /analyze HTTP/1.1" 500 -
</code>
<code>The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_API_KEY environment variable 127.0.0.1 - - [04/May/2024 20:55:38] "POST /analyze HTTP/1.1" 500 - </code>
The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_API_KEY environment variable

127.0.0.1 - - [04/May/2024 20:55:38] "POST /analyze HTTP/1.1" 500 -

Could someone please help me here?

I have tried what was given by some on SO. But it just does not work.

  • javascript
  • python
  • flask
  • openai-api

3

Plain text
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EnlighterJS 3 Syntax Highlighter
<code>from flask import Flask, request, jsonify, send_from_directory
import openai
from openai import OpenAI
import os
app = Flask(__name__)
@app.route('/')
def index():
return send_from_directory('.', 'index.html')
@app.route('/analyze', methods=['POST'])
def analyze_cv():
if 'cv' not in request.files or 'job_description' not in request.files:
return jsonify({'error': 'CV and Job Description files are required'}), 400
cv = request.files['cv'].read()
job_description = request.files['job_description'].read()
try:
cv = cv.decode('utf-8')
except UnicodeDecodeError:
cv = cv.decode('latin-1')
try:
job_description = job_description.decode('utf-8')
except UnicodeDecodeError:
job_description = job_description.decode('latin-1')
prompt = f"Given the following job description:n{job_description}nnAssess the suitability of the candidate based on the CV:n{cv}nnScore the candidate from 1 to 10 and provide an explanation."
try:
client = OpenAI(api_key='sk-')
response = client.completions.create(
model="gpt-3.5-turbo-instruct",
prompt=prompt,
temperature=0.7,
max_tokens=1024
)
score = int(response.choices[0].text.split()[0])
explanation = response.choices[0].text.split('n')[1]
return jsonify({'score': score, 'explanation': explanation}), 200
except Exception as e:
print(e)
return jsonify({'error': 'An error occurred while analyzing the CV'}), 500
if __name__ == '__main__':
app.run(debug=True)
</code>
<code>from flask import Flask, request, jsonify, send_from_directory import openai from openai import OpenAI import os app = Flask(__name__) @app.route('/') def index(): return send_from_directory('.', 'index.html') @app.route('/analyze', methods=['POST']) def analyze_cv(): if 'cv' not in request.files or 'job_description' not in request.files: return jsonify({'error': 'CV and Job Description files are required'}), 400 cv = request.files['cv'].read() job_description = request.files['job_description'].read() try: cv = cv.decode('utf-8') except UnicodeDecodeError: cv = cv.decode('latin-1') try: job_description = job_description.decode('utf-8') except UnicodeDecodeError: job_description = job_description.decode('latin-1') prompt = f"Given the following job description:n{job_description}nnAssess the suitability of the candidate based on the CV:n{cv}nnScore the candidate from 1 to 10 and provide an explanation." try: client = OpenAI(api_key='sk-') response = client.completions.create( model="gpt-3.5-turbo-instruct", prompt=prompt, temperature=0.7, max_tokens=1024 ) score = int(response.choices[0].text.split()[0]) explanation = response.choices[0].text.split('n')[1] return jsonify({'score': score, 'explanation': explanation}), 200 except Exception as e: print(e) return jsonify({'error': 'An error occurred while analyzing the CV'}), 500 if __name__ == '__main__': app.run(debug=True) </code>
from flask import Flask, request, jsonify, send_from_directory
import openai
from openai import OpenAI
import os

app = Flask(__name__)

@app.route('/')
def index():
    return send_from_directory('.', 'index.html')

@app.route('/analyze', methods=['POST'])
def analyze_cv():
    if 'cv' not in request.files or 'job_description' not in request.files:
        return jsonify({'error': 'CV and Job Description files are required'}), 400

    cv = request.files['cv'].read()
    job_description = request.files['job_description'].read()

    try:
        cv = cv.decode('utf-8')
    except UnicodeDecodeError:
        cv = cv.decode('latin-1')

    try:
        job_description = job_description.decode('utf-8')
    except UnicodeDecodeError:
        job_description = job_description.decode('latin-1')

    prompt = f"Given the following job description:n{job_description}nnAssess the suitability of the candidate based on the CV:n{cv}nnScore the candidate from 1 to 10 and provide an explanation."

    try:
        client = OpenAI(api_key='sk-')
        response = client.completions.create(
            model="gpt-3.5-turbo-instruct",
            prompt=prompt,
            temperature=0.7,
            max_tokens=1024
        )

        score = int(response.choices[0].text.split()[0])
        explanation = response.choices[0].text.split('n')[1]

        return jsonify({'score': score, 'explanation': explanation}), 200
    
    except Exception as e:
        print(e)  
        return jsonify({'error': 'An error occurred while analyzing the CV'}), 500

if __name__ == '__main__':
    app.run(debug=True)

This code now works. But another error pops up
127.0.0.1 – – [04/May/2024 22:29:55] “GET / HTTP/1.1” 304 –
Error code: 429 – {‘error’: {‘message’: ‘You exceeded your current quota, please check your plan and billing details. For more information on this error, read the docs: https://platform.openai.com/docs/guides/error-codes/api-errors.’, ‘type’: ‘insufficient_quota’, ‘param’: None, ‘code’: ‘insufficient_quota’}}
127.0.0.1 – – [04/May/2024 22:30:17] “POST /analyze HTTP/1.1” 500 –

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Filed under: Kiến thức lập trình - @ 00:06

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