refactor imports

app.py

@@ -1,41 +1,13 @@
-# import os
-# from datetime import datetime
-
-# from flask import Flask, redirect, render_template, request, url_for
-# from model import initialize_index
-
-# app = Flask(__name__)
-# index = None
-
-# @app.route("/")
-# def home():
-#     return render_template('chat.html')
-
-# @app.route("/get")
-# def get_bot_response():
-#     query_text = request.args.get("msg", None)
-#     if query_text is None:
-#         return "Invalid input"
-#     response = index.query(query_text)
-#     return str(response), 200
-
-# if __name__ == "__main__":
-#     index = initialize_index("index.json")
-#     app.run(debug=True)
 import os
 from datetime import datetime
 
 import openai
 from flask import Flask, redirect, render_template, request, session, url_for
-from model import initialize_index
+from models.bloom import initialize_index
 
 app = Flask(__name__)
-app.secret_key = os.environ.get('SECRET_KEY', 'my-secret-key')
 index = None
 
-# Set up OpenAI authentication
-# openai.api_key = os.environ.get('OPENAI_API_KEY')
-
 @app.route("/")
 def home():
     if 'access_token' not in session:

model/model.py

@@ -1,25 +0,0 @@
-import os
-
-import openai
-from customLLM import CustomLLM, prompt_helper
-from data import data
-from dotenv import load_dotenv
-from llama_index import (Document, GPTSimpleVectorIndex, LLMPredictor,
-                         ServiceContext)
-
-load_dotenv()
-
-# openai.api_key = os.getenv("OPENAI_API_KEY")
-
-#define our llm
-llm_predictor = LLMPredictor(llm=CustomLLM())
-service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
-
-def initialize_index(index_name):
-    if os.path.exists(index_name):
-        return GPTSimpleVectorIndex.load_from_disk(index_name)
-    else:
-        documents = [Document(d) for d in data]
-        index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)
-        index.save_to_disk(index_name)
-        return index

model.py → models/bloom.py

@@ -1,15 +1,13 @@
 import os
 
-import openai
 from customLLM import CustomLLM, prompt_helper
-from data import data
 from dotenv import load_dotenv
 from llama_index import (Document, GPTSimpleVectorIndex, LLMPredictor,
                          ServiceContext)
 
-load_dotenv()
+from data.regItems import data
 
-# openai.api_key = os.getenv("OPENAI_API_KEY")
+load_dotenv()
 
 #define our llm
 llm_predictor = LLMPredictor(llm=CustomLLM())