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import gradio as gr
import openai
import os
import re
from datetime import datetime
# Put your OpenAI API key here
openai.api_key = os.getenv('OpenAPI')
def gpt3_query(prompt, engine='gpt-3.5-turbo-16k', max_tokens=15050, temperature=0.5, retries=5):
for i in range(retries):
try:
response = openai.ChatCompletion.create(
model=engine,
messages=[{"role": "system", "content": "You are a creative writer who specializes in crafting engaging stories to sell products or services"},
{"role": "user", "content": prompt}],
max_tokens=max_tokens,
n=1,
temperature=temperature
)
return response.choices[0].message['content'].strip()
except Exception as e:
if i < retries - 1: # i is zero indexed
continue
else:
return f"Error in gpt3_query after {retries} attempts: {str(e)}"
def read_file(file_path):
with open(file_path, 'r') as file:
return file.read()
def write_file(file_path, content):
with open(file_path, 'w') as file:
file.write(content)
def generate_story(goal_string):
writer_path = "writer.txt"
editor_path = "editor.txt"
writer_description = read_file(writer_path)
editor_description = read_file(editor_path)
match = re.search(r"Max word count: (\d+)", writer_description)
if match:
max_word_count = int(match.group(1))
else:
return "Error: Max word count not found in writer.txt", None
writer_prompt = f"""Create an engaging and persuasive story that promotes a product or service, based on this goal: {goal_string}. Include a 'step-by-step day in the life' format, showing how the product or service impacts the main character's daily life."""
part = gpt3_query(writer_prompt, temperature=0.7) # Adjust temperature for more focused output
writer_prompt = f"Expand the outline with a detailed story. Complete each section for this outline: {part} "
next_part = gpt3_query(writer_prompt, temperature=0.7) # Adjust temperature for more focused output
new_story1 = next_part
iteration = 0
story = ""
critique = ""
while True:
iteration += 1
if iteration >= 4:
break
editor_prompt = f"With the persona of: {editor_description} \n Provide a detailed critique of this story: {new_story1}"
critique = gpt3_query(editor_prompt, max_tokens=6050)
writer_prompt = f"Please rewrite the story accommodating the editor's critique. If a section has no feedback, include the unedited version in the output. Current story: {new_story1}\n\nEditor's critique:\n{critique}"
next_part1 = gpt3_query(writer_prompt, temperature=0.7, max_tokens=6050) # Adjust temperature for more focused output
new_story = next_part1
new_word_count = len(new_story.split())
story = new_story1
#Save the optimized story to a file
# Generate a unique filename by appending a timestamp
timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
filename = f"Personal_Story_{timestamp}.txt"
write_file("PersonalStory.txt", story)
return "\nPersona Day In the Life of Story Saved", "PersonalStory.txt"
iface = gr.Interface(
fn=generate_story,
inputs=gr.inputs.Textbox(default="Provide an easy-to-read and factual sales story answering: How can the 'persona' utilize 'solution or product' in their daily job? Include an overview, background, then the day in the life of."),
outputs=["text", gr.components.File()]
)
iface.launch() |