Refactoring: modular configuration, separated learning and response logic

pyproject.toml

@@ -22,6 +22,7 @@ flake8 = "^6.0.0"
 
 [tool.poetry.scripts]
 chadgpt = "chadgpt.main:main"
+learn = "chadgpt.main:learn"
 
 
 [build-system]

src/chadgpt/main.py

@@ -1,77 +1,14 @@
 #!/usr/bin/env python3
-import os
+from .config import DB_PATH
-import gradio as gr
+from .indexer import construct_index
-from dotenv import load_dotenv
+from .interface import iface
-from langchain.chat_models import ChatOpenAI
-from gpt_index import (
-    SimpleDirectoryReader,
-    GPTSimpleVectorIndex,
-    LLMPredictor,
-    PromptHelper
-)
 
 
-def get_env():
+def learn():
-    if not os.environ.get("OPENAI_API_KEY"):
+    construct_index(DB_PATH)
-        load_dotenv()
-
-
-# parse hidden api key:
-def construct_index(directory_path):
-    # promt params:
-    max_input_size = 4096
-    num_outputs = 512
-    max_chunk_overlap = 20
-    chunk_size_limit = 600
-
-    prompt_helper = PromptHelper(
-        max_input_size,
-        num_outputs,
-        max_chunk_overlap,
-        chunk_size_limit=chunk_size_limit
-    )
-
-    llm = ChatOpenAI(
-        temperature=0.7,
-        model_name="gpt-3.5-turbo",
-        max_tokens=num_outputs
-    )
-
-    llm_predictor = LLMPredictor(llm)
-
-    # get documents for learn:
-    documents = SimpleDirectoryReader(directory_path).load_data()
-
-    index = GPTSimpleVectorIndex(
-        documents,
-        llm_predictor=llm_predictor,
-        prompt_helper=prompt_helper
-    )
-
-    index_file = os.environ.get('DB_PATH') + "/index.json"
-    index.save_to_disk(index_file)
-
-    return index
-
-
-def chatbot(input_text):
-    index_file = os.environ.get("DB_PATH") + "/index.json"
-    index = GPTSimpleVectorIndex.load_from_disk(index_file)
-    response = index.query(input_text, response_mode="compact")
-    return response.response
-
-
-iface = gr.Interface(
-    fn=chatbot,
-    inputs=gr.components.Textbox(lines=7, label="Enter your text"),
-    outputs="text",
-    title="ISPsystem custom-trained AI Chatbot"
-)
 
 
 def main():
-    get_env()
-    construct_index(os.environ.get("DB_PATH"))
     iface.launch(share=False)