Spaces:
Running
Running
File size: 1,495 Bytes
1867fb4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 |
from flask import Flask, render_template, request
from dotenv import load_dotenv, find_dotenv
from openai import OpenAI
load_dotenv(find_dotenv())
app = Flask(__name__)
client = OpenAI()
def generate_post(tone, topic, length, instructions):
prompt = f"Write a {tone} blog post about {topic}. Make sure the blog post is no longer than {length} words and ends with a conclusion. {instructions}"
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": f"You are an expert {tone} blogger and creative writer."},
{"role": "user", "content": prompt}
],
max_tokens=600,
temperature=0.7,
top_p=0.9,
frequency_penalty=0,
presence_penalty=0,
stop=["In conclusion", "In summary"]
)
return response.choices[0].message.content.strip()
@app.route('/')
def index():
return render_template('index.html')
@app.route('/generate', methods=['POST'])
def generate_blog():
# Retrieve form data
topic = request.form.get('topic')
tone = request.form.get('tone')
length = request.form.get('length')
instructions = request.form.get('instructions')
# Here, you would add your model inference code to generate the blog post
generated_text = generate_post(tone, topic, length, instructions)
return render_template('results.html', generated_text=generated_text)
if __name__ == '__main__':
app.run(debug=True)
|