header fix?

main.py

@@ -3,7 +3,8 @@
 import os
 import Paginator
 from model import main
-
+from dotenv import load_dotenv
+load_dotenv()
 intents = discord.Intents.default()
 intents.message_content = True
 intents.members = True
@@ -32,4 +33,4 @@ async def peer(ctx, arg):
    new_embed = discord.Embed(title="Not The Right Channel!", description=output, color=3447003)
    return await ctx.send(embed=new_embed)
 
-bot.run(os.environ['BOT_TOKEN'])
+bot.run(os.getenv('product_spec_bot'))

model.py

@@ -1,10 +1,12 @@
+from dotenv import load_dotenv
 from notion_extraction import extract_product_spec_text, parse_product_spec_text, extract_id_from_url
 from prompts import happy_path, milestones, problem, schedule, solution, success_criteria, target_users, tech_stack
 import requests
 import os
 import openai
-
-openai.api_key = os.environ['OPENAI_API_KEY1']
+from dotenv import load_dotenv
+load_dotenv()
+openai.api_key = os.getenv('OPENAI_API_KEY1')
 
 def main(url):
 
@@ -56,4 +58,4 @@ def main(url):
 
     return feedback_summary["choices"][0]["text"]
     """
-    return feedbacks
+    return feedbacks

notion_extraction.py

@@ -1,7 +1,12 @@
 import requests
 import os
+from dotenv import load_dotenv
+import openai
+import time
 
-notion_token = os.environ['NOTION_TOKEN1']
+
+load_dotenv()
+notion_token = os.getenv('NOTION_TOKEN')
 
 def extract_id_from_url(url): 
     return url.split('-')[-1]
@@ -69,15 +74,39 @@ def match_name_to_label(name): # Matches a name to a product spec label
     highest_similarity_score = 0
     most_similar = None
 
-    for label in labels: # Finds the label with the highest similarity score
+    openai.api_key = os.getenv("OPENAI_API_KEY1")
+
+    labels = ['Problem Statement', 'Solution Statement', 'Who Has This Problem?', 'Success Criteria', 'Success Metrics', 'Milestones', 'Schedule of Deliverables', 'Tech Stack', 'Happy Path']
+
+    questionable_header = name
+    response = openai.Completion.create(
+    model="text-curie-001",
+    prompt=f"prompt: answer the following question only using the header_list, then say where they are in the list\n\nheader_list: 'Problem Statement', 'Solution Statement', 'Who Has This Problem?', 'Success Criteria', 'Success Metrics', 'Milestones', 'Schedule of Deliverables', 'Tech Stack', 'Happy Path'\n\nquestion: out of the header_list which one is most similar to the header \"Other Ideas Further areas of improvement:\"?\n\noutput: \"Other Ideas Further areas of improvement\" is most similar to the header \"Milestones\". milestones is the sixth in header_list\n\nquestion: out of the header_list which one is most similar to the header \"Success Criteria\"?\n\noutput: \"Success Criteria\" is most similar to the header \"Success Criteria\" Success Criteria is the fourth in header_list.\n\nquestion: out of the header_list which one is most similar to the header \"What The Product Will Not Do\"?\n\noutput: \"What The Product Will Not Do\" is most similar to the header  \"Success Criteria\" Success Criteria is the fourth in header_list.\n\nquestion: out of the header_list which one is most similar to the header \"{questionable_header}\"?\n\noutput: \"{questionable_header}\" is most similar to the header  ",
+    temperature=1,
+    max_tokens=75,
+    top_p=1,
+    frequency_penalty=0,
+    presence_penalty=0
+    )
+    corrected_header = response["choices"][0]["text"]
+    redone_lables = corrected_header.replace('"',"")
+    for x in labels:
+       if x in redone_lables: 
+            print('ai generated labels worked', x)
+            return x
+
 
+    for y in range(2): print('jacard runnging as backup for[gtp3 failure]')
+    print(f"'{redone_lables}' is the out come of '{questionable_header}' delete this line of code later")
+    for label in labels: # Finds the label with the highest similarity score
+
         similarity_score = jaccard_similarity(label.split(), name.split())
-
+                
         if similarity_score > highest_similarity_score:
-
+                    
             most_similar = label
             highest_similarity_score = similarity_score
-
+            
     return most_similar