@ -26,6 +26,14 @@ app = Flask(__name__)
## Set authentication on your APIs
## Let's at least have some kind of auth
# Load your OpenAI API key from a file
with open ( " openai.key " , " r " ) as key_file :
api_key = key_file . read ( ) . strip ( )
## Define our own client
client = openai . OpenAI ( api_key = api_key )
# Read API tokens from the apikeys.json file
with open ( " fabric_api_keys.json " , " r " ) as tokens_file :
valid_tokens = json . load ( tokens_file )
@ -84,10 +92,6 @@ def fetch_content_from_url(url):
return str ( e )
# Set your OpenAI API key
with open ( " openai.key " , " r " ) as key_file :
openai . api_key = key_file . read ( ) . strip ( )
## APIs
@ -117,7 +121,7 @@ def extwis():
user_message = { " role " : " user " , " content " : user_file_content + " \n " + input_data }
messages = [ system_message , user_message ]
try :
response = openai . ChatCompletion . create (
response = openai . chat. completions . create (
model = " gpt-4-1106-preview " ,
messages = messages ,
temperature = 0.0 ,
@ -125,92 +129,11 @@ def extwis():
frequency_penalty = 0.1 ,
presence_penalty = 0.1 ,
)
assistant_message = response [ " choices " ] [ 0 ] [ " message " ] [ " content " ]
assistant_message = response . choices [ 0 ] . message . content
return jsonify ( { " response " : assistant_message } )
except Exception as e :
return jsonify ( { " error " : str ( e ) } ) , 500
# /labelandrate
@app.route ( " /labelandrate " , methods = [ " POST " ] )
def labelandrate ( ) :
data = request . get_json ( )
if " input " not in data :
return jsonify ( { " error " : " Missing input parameter " } ) , 400
input_data = data [ " input " ]
system_message = {
" role " : " system " ,
" content " : """
You are an ultra - wise and brilliant classifier and judge of content . You label content with a a comma - separated list of single - word labels and then give it a quality rating .
Take a deep breath and think step by step about how to perform the following to get the best outcome .
STEPS :
1. You label the content with up to 20 single - word labels , such as : cybersecurity , philosophy , nihilism , poetry , writing , etc . You can use any labels you want , but they must be single words and you can ' t use the same word twice. This goes in a section called LABELS:.
2. You then rate the content based on the number of ideas in the input ( below ten is bad , between 11 and 20 is good , and above 25 is excellent ) combined with how well it matches the THEMES of : human meaning , the future of AI , mental models , abstract thinking , unconvential thinking , meaning in a post - ai world , continuous improvement , reading , art , books , and related topics .
You use the following rating levels :
S Tier ( Must Consume Original Content Immediately ) : 18 + ideas and / or STRONG theme matching with the themes in STEP #2.
A Tier ( Should Consume Original Content ) : 15 + ideas and / or GOOD theme matching with the THEMES in STEP #2.
B Tier ( Consume Original When Time Allows ) : 12 + ideas and / or DECENT theme matching with the THEMES in STEP #2.
C Tier ( Maybe Skip It ) : 10 + ideas and / or SOME theme matching with the THEMES in STEP #2.
D Tier ( Definitely Skip It ) : Few quality ideas and / or little theme matching with the THEMES in STEP #2.
Also provide a score between 1 and 100 for the overall quality ranking , where 100 is a perfect match with the highest number of high quality ideas , and 1 is the worst match with a low number of the worst ideas .
The output should look like the following :
LABELS :
Cybersecurity , Writing , Running , Copywriting
RATING :
S Tier : ( Must Consume Original Content Immediately )
Explanation : $ $ Explanation in 5 short bullets for why you gave that rating . $ $
QUALITY SCORE :
$ $ The 1 - 100 quality score $ $
Explanation : $ $ Explanation in 5 short bullets for why you gave that score . $ $
""" ,
}
user_message = {
" role " : " user " ,
" content " : """
CONTENT :
""" ,
}
messages = [ system_message , { " role " : " user " , " content " : input_data } ]
try :
response = openai . ChatCompletion . create (
model = " gpt-4-1106-preview " ,
messages = messages ,
temperature = 0.0 ,
top_p = 1 ,
frequency_penalty = 0.1 ,
presence_penalty = 0.1 ,
)
assistant_message = response [ " choices " ] [ 0 ] [ " message " ] [ " content " ]
return jsonify ( { " response " : assistant_message } )
except Exception as e :
return jsonify ( { " error " : str ( e ) } ) , 500
# Run the application
if __name__ == " __main__ " :
app . run ( host = " 1.1.1.1 " , port = 13337 , debug = True )
Daniel Miessler
8 months ago