Added linting; format workflow; formatted code

.github/workflows/format.yml

@@ -0,0 +1,26 @@
+name: Check formatting
+
+on:
+  push:
+    branches:
+      - '*'
+  pull_request:
+    branches:
+      - '*'
+  workflow_dispatch:
+
+jobs:
+  test:
+    runs-on: ubuntu-20.04
+    steps:
+      - uses: actions/checkout@v1
+      - name: Set up Python 3.9
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.9
+      
+      - name: Install lint dependencies
+        run: pip install black==22.3.0 isort==5.10.1 flake8==4.0.1
+
+      - name: Lint the code
+        run: sh shell/lint.sh

app.py

@@ -1,22 +1,22 @@
-import streamlit as st
-from llama_index import VectorStoreIndex, ServiceContext, set_global_service_context
-from llama_index.llms import AzureOpenAI
-from llama_index.embeddings import OpenAIEmbedding
 import json
 import os
-from llama_index import SimpleDirectoryReader
 
+import streamlit as st
+from llama_index import ServiceContext
+from llama_index import SimpleDirectoryReader
+from llama_index import VectorStoreIndex
+from llama_index import set_global_service_context
+from llama_index.embeddings import OpenAIEmbedding
+from llama_index.llms import AzureOpenAI
 
 # Initialize message history
 st.header("Chat with André's research 💬 📚")
 
-if "messages" not in st.session_state.keys(): # Initialize the chat message history
-    st.session_state.messages = [
-        {"role": "assistant", "content": "Ask me a question about André's research!"}
-    ]
+if "messages" not in st.session_state.keys():  # Initialize the chat message history
+    st.session_state.messages = [{"role": "assistant", "content": "Ask me a question about André's research!"}]
 
 # Load config values
-with open(r'config.json') as config_file:
+with open(r"config.json") as config_file:
     config_details = json.load(config_file)
 
 
@@ -29,23 +29,26 @@ def load_data():
             engine=config_details["ENGINE"],
             temperature=0.5,
             api_key=os.getenv("OPENAI_API_KEY"),
-            api_base=config_details['OPENAI_API_BASE'],
+            api_base=config_details["OPENAI_API_BASE"],
             api_type="azure",
-            api_version=config_details['OPENAI_API_VERSION'],
-            system_prompt="You are an expert on André's research and your job is to answer technical questions. Assume that all questions are related to André's research. Keep your answers technical and based on facts – do not hallucinate features."
+            api_version=config_details["OPENAI_API_VERSION"],
+            system_prompt="You are an expert on André's research and your job is to answer"
+            "technical questions. Assume that all questions are related to"
+            "André's research. Keep your answers technical and based on facts"
+            " – do not hallucinate features.",
         )
         # You need to deploy your own embedding model as well as your own chat completion model
         embed_model = OpenAIEmbedding(
             model="text-embedding-ada-002",
             deployment_name=config_details["ENGINE_EMBEDDING"],
             api_key=os.getenv("OPENAI_API_KEY"),
-            api_base=config_details['OPENAI_API_BASE'],
+            api_base=config_details["OPENAI_API_BASE"],
             api_type="azure",
-            api_version=config_details['OPENAI_API_VERSION'],
+            api_version=config_details["OPENAI_API_VERSION"],
         )
         service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model)
         set_global_service_context(service_context)
-        index = VectorStoreIndex.from_documents(documents) #, service_context=service_context)
+        index = VectorStoreIndex.from_documents(documents)  # , service_context=service_context)
         return index
 
 
@@ -53,10 +56,10 @@ def main():
     index = load_data()
     chat_engine = index.as_chat_engine(chat_mode="condense_question", verbose=True)
 
-    if prompt := st.chat_input("Your question"): # Prompt for user input and save to chat history
+    if prompt := st.chat_input("Your question"):  # Prompt for user input and save to chat history
         st.session_state.messages.append({"role": "user", "content": prompt})
 
-    for message in st.session_state.messages: # Display the prior chat messages
+    for message in st.session_state.messages:  # Display the prior chat messages
         with st.chat_message(message["role"]):
             st.write(message["content"])
 
@@ -67,7 +70,7 @@ def main():
                 response = chat_engine.chat(prompt)
                 st.write(response.response)
                 message = {"role": "assistant", "content": response.response}
-                st.session_state.messages.append(message) # Add response to message history
+                st.session_state.messages.append(message)  # Add response to message history
 
 
 if __name__ == "__main__":

setup.cfg

@@ -0,0 +1,14 @@
+[metadata]
+description-file = README.md
+
+[isort]
+force_single_line=True
+known_first_party=gradient_accumulator
+line_length=120
+profile=black
+
+[flake8]
+# imported but unused in __init__.py, that's ok.
+per-file-ignores=*__init__.py:F401
+ignore=E203,W503,W605,F632,E266,E731,E712,E741
+max-line-length=120

shell/format.sh

@@ -0,0 +1,4 @@
+#!/bin/bash
+isort --sl app.py
+black --line-length 120 app.py
+flake8 app.py

shell/lint.sh

@@ -0,0 +1,23 @@
+#!/bin/bash
+isort --check --sl -c app.py
+if ! [ $? -eq 0 ]
+then
+  echo "Please run \"sh shell/format.sh\" to format the code."
+  exit 1
+fi
+echo "no issues with isort"
+flake8 app.py
+if ! [ $? -eq 0 ]
+then
+  echo "Please fix the code style issue."
+  exit 1
+fi
+echo "no issues with flake8"
+black --check --line-length 120 app.py
+if ! [ $? -eq 0 ]
+then
+  echo "Please run \"sh shell/format.sh\" to format the code."
+    exit 1
+fi
+echo "no issues with black"
+echo "linting success!"