Mainly formatting updates; to v0.2.5

app.py

@@ -1,4 +1,3 @@
-import streamlit
 import streamlit as st
 
 # st.set_page_config(layout="wide")
@@ -8,13 +7,12 @@ from ring import LLM_LLAMA2
 
 from ring import PREF_ORDER_LLMS
 
-
 from ring import requires_openai_key
 from ring import get_openai_api_key
 from ring import generate_response
 from ring import generate_panel_response
 
-__version__ = "0.2.4"
+__version__ = "0.2.5"
 
 col1, col2 = st.columns([3, 1])
 
@@ -40,11 +38,9 @@ tab_free, tab_panel, tab_pubs, tab_data, tab_corpus, tab_model, tab_about = st.t
 
 
 with tab_free:
-
     st.markdown("**Ask individual LLMs questions about _C. elegans_**")
 
     with st.form("form_free"):
-
         text = st.text_area(
             "Ask a question related to _C. elegans_:",
             "What is the primary role of the C. elegans neuron AVBL?",
@@ -68,11 +64,9 @@ with tab_free:
             st.info(response)
 
 with tab_panel:
-
     st.markdown("**Get a consensus answer across multiple LLMs**")
 
     with st.form("form_panel"):
-
         text = st.text_area(
             "Ask a question related to _C. elegans_:",
             "What is the typical length of the worm C. elegans?",
@@ -132,7 +126,6 @@ with tab_panel:
             st.info(response)
 
 with tab_pubs:
-
     from publications import find_basis_paper
 
     st.markdown(
@@ -140,7 +133,6 @@ with tab_pubs:
     )
 
     with st.form("form_pubs"):
-
         text = st.text_input("Enter a topic:", "C. elegans locomotion")
 
         num = st.text_input("Number of results", value=10)
@@ -154,11 +146,9 @@ with tab_pubs:
 
 
 with tab_data:
-
     st.markdown("**Query structured datasets**")
 
     with st.form("form_data"):
-
         from datasources import DS_WORMNEUROATLAS
         from datasources import FORMATS
         from datasources import query_data_source
@@ -176,7 +166,6 @@ with tab_data:
             st.info(response)
 
 with tab_corpus:
-
     from corpus_query import run_query
 
     st.markdown(
@@ -184,7 +173,6 @@ with tab_corpus:
     )
 
     with st.form("form_corpus"):
-
         text = st.text_input(
             "Enter a query:",
             "What types of ion channels are present in C. elegans neurons?",
@@ -202,11 +190,9 @@ with tab_corpus:
             st.info(response)
 
 with tab_model:
-
     st.markdown("**Run a _C. elegans_ cell model**")
 
     with st.form("form_model"):
-
         from model import run_model
         from model import MODELS
 
@@ -219,8 +205,6 @@ with tab_model:
             response, traces, events = run_model(text, model_to_sim)
             st.info(response)
             try:
-
-                import numpy as np
                 import matplotlib.pyplot as plt
 
                 fig, ax = plt.subplots()
@@ -240,7 +224,6 @@ with tab_model:
                 st.info("There was a problem...\n\n%s" % e)
 
 with tab_about:
-
     st.markdown('### About "Project Sydney"')
     st.markdown(
         "This is an initiative by the [OpenWorm project](https://openworm.org) to investigate the use of LLMs for interacting with scientific literature and structured datasets related to _C. elegans_."

datasources.py

@@ -2,8 +2,8 @@
 #
 #
 
-import os
-import re
+import wormneuroatlas as wa
+from pprint import PrettyPrinter
 
 FORMAT_TEXT = "Text"
 FORMAT_DICT = "Dict"
@@ -13,16 +13,12 @@ FORMATS = (FORMAT_TEXT, FORMAT_DICT)
 
 DS_WORMNEUROATLAS = "WormNeuroAtlas"
 
-import wormneuroatlas as wa
-import pprint
 
-pp = pprint.PrettyPrinter(depth=6)
+pp = PrettyPrinter(depth=6)
 
 
 def query_data_source(text, source, format):
-
     if source == DS_WORMNEUROATLAS:
-
         atlas = wa.NeuroAtlas()
         ds, ss = atlas.everything_about(
             text, return_values=True, return_text=True, print_text=False
@@ -37,16 +33,12 @@ def query_data_source(text, source, format):
             info += """```%s```""" % ss
         elif format == FORMAT_DICT:
             info = """```
-%s```""" % pp.pformat(
-                ds
-            )
+%s```""" % pp.pformat(ds)
         elif format == FORMAT_JSON:
             import json
 
             jj = json.dumps(ds)
             info = """```
-%s```""" % pp.pformat(
-                jj
-            )
+%s```""" % pp.pformat(jj)
 
     return info

model.py

@@ -2,9 +2,6 @@
 #
 #
 
-import os
-import re
-
 from neuromllite.utils import load_simulation_json
 from neuromllite.utils import load_network_json
 from neuromllite.NetworkGenerator import generate_and_run
@@ -22,7 +19,6 @@ MODELS = {
 
 
 def run_model(text, model, verbose=False):
-
     info = """Running [%s] with parameter: [%s]...
 
 """ % (
@@ -41,18 +37,12 @@ def run_model(text, model, verbose=False):
     net.parameters["stim_amp"] = text.strip()
 
     if verbose:
-        info += (
-            """```%s```
-
-"""
-            % sim.to_json()
-        )
-        info += (
-            """```%s```
-
-"""
-            % net.to_json()
-        )
+        info += """```%s```
+
+""" % sim.to_json()
+        info += """```%s```
+
+""" % net.to_json()
 
     traces, events = generate_and_run(
         sim,

publications.py

@@ -3,7 +3,6 @@
 #
 
 import os
-import re
 import traceback
 
 import requests
@@ -13,7 +12,6 @@ result_limit = 10
 
 
 def find_basis_paper(query, result_limit=10):
-
     papers = None
     info = """Search results: 
 
@@ -59,7 +57,7 @@ def get_element(paper, part):
     if part in simple_parts:
         if part in paper and paper[part] is not None:
             v = paper[part]
-            return v.strip() if type(v) == str else v
+            return v.strip() if isinstance(v, str) else v
         else:
             return "???"
 
@@ -91,7 +89,6 @@ def format_paper_list(all_papers, result_limit):
 
     print(sorted_papers)
     for idx, paper in enumerate(sorted_papers):
-
         list += f"{idx}: **{get_element(paper, 'authors')} et al. {get_element(paper, 'year')}**, [{get_element(paper, 'journal')}]({get_element(paper, 'url')}) {get_element(paper, 'title')}. Cited by {get_element(paper, 'citationCount')}\n\n"
         if paper["tldr"] and paper["tldr"]["text"]:
             list += f"> _{paper['tldr']['text'].strip()}_ \n\n"

requirements.txt

@@ -13,4 +13,5 @@ paper-qa
 langchain-google-genai
 langchain_ai21
 langchain_anthropic
-langchain_cohere
+langchain_cohere
+streamlit

ring.py

@@ -27,15 +27,13 @@ PREF_ORDER_LLMS = (
 
 
 def requires_openai_key(llm_ver):
-
     return llm_ver in OPENAI_LLMS
 
 
 def get_openai_api_key():
-
     # if openai_api_key_sb == None or len(openai_api_key_sb)==0:
     openai_api_key = os.environ.get("OPENAI_API_KEY")
-    if openai_api_key == None:
+    if openai_api_key is None:
         openai_api_key = str(open("../oaik", "r").readline())
     # else:
     #    openai_api_key = openai_api_key_sb
@@ -44,42 +42,36 @@ def get_openai_api_key():
 
 
 def get_llamaapi_key():
-
     llamaapi_key = os.environ.get("LLAMAAPI_KEY")
 
     return llamaapi_key
 
 
 def get_gemini_api_key():
-
     gemini_api_key = os.environ.get("GEMINIAPI_KEY")
 
     return gemini_api_key
 
 
 def get_ai21_api_key():
-
     ai21_api_key = os.environ.get["AI21_API_KEY"]
 
     return ai21_api_key
 
 
 def get_anthropic_key():
-
     anthropic_api_key = os.environ.get["ANTHROPIC_API_KEY"]
 
     return anthropic_api_key
 
 
 def get_cohere_key():
-
     cohere_api_key = os.environ.get["COHERE_API_KEY"]
 
     return cohere_api_key
 
 
 def get_llm(llm_ver, temperature):
-
     if llm_ver == LLM_GPT35:
         from langchain_openai import OpenAI
 
@@ -119,7 +111,6 @@ def get_llm(llm_ver, temperature):
         llm = ChatLlamaAPI(client=llama)
 
     elif llm_ver == LLM_GEMINI:
-
         from langchain_google_genai import ChatGoogleGenerativeAI
 
         llm = ChatGoogleGenerativeAI(
@@ -127,19 +118,16 @@ def get_llm(llm_ver, temperature):
         )
 
     elif llm_ver == LLM_AI21:
-
         from langchain_ai21 import AI21LLM
 
         llm = AI21LLM(model="j2-ultra")
 
     elif llm_ver == LLM_CLAUDE2:
-
         from langchain_anthropic import AnthropicLLM
 
         llm = AnthropicLLM(model="claude-2.1")
 
     elif llm_ver == LLM_COHERE:
-
         from langchain_cohere import ChatCohere
 
         llm = ChatCohere()
@@ -160,7 +148,6 @@ GENERAL_QUERY_LIMITED_PROMPT_TEMPLATE = """You are a neuroscientist who is answe
 
 
 def generate_response(input_text, llm_ver, temperature, only_celegans):
-
     template = (
         GENERAL_QUERY_LIMITED_PROMPT_TEMPLATE
         if only_celegans
@@ -181,11 +168,9 @@ def generate_response(input_text, llm_ver, temperature, only_celegans):
 
 
 def generate_panel_response(input_text, llm_panelists, llm_panel_chair, temperature):
-
     responses = {}
 
     for llm_ver in llm_panelists:
-
         prompt = PromptTemplate(
             template=GENERAL_QUERY_PROMPT_TEMPLATE, input_variables=["question"]
         )
@@ -199,9 +184,7 @@ def generate_panel_response(input_text, llm_panelists, llm_panel_chair, temperat
     panel_chair_prompt = """You are a neuroscientist chairing a panel discussion on the nematode C. elegans. A researcher has asked the following question:
     {question}
     and %i experts on the panel have give their answers. 
-    """ % (
-        len(llm_panelists)
-    )
+    """ % (len(llm_panelists))
 
     for llm_ver in llm_panelists:
         panel_chair_prompt += """

test.py

@@ -1,5 +1,3 @@
-from ring import LLM_GPT35
-from ring import LLM_GPT4
 from ring import LLM_GPT4o
 from ring import LLM_LLAMA2
 from ring import LLM_GEMINI
@@ -7,7 +5,6 @@ from ring import LLM_AI21
 from ring import LLM_CLAUDE2
 from ring import LLM_COHERE
 
-from ring import PREF_ORDER_LLMS
 
 from ring import generate_response