Update app.py

app.py

@@ -1,39 +1,37 @@
-import streamlit as st
-import streamlit.components.v1 as components
-import os
-import json
-import random
 import base64
+import cv2
 import glob
+import json
 import math
+import os
 import pytz
+import random
 import re
 import requests
+import streamlit as st
+import streamlit.components.v1 as components
+import textract
 import time
 import zipfile
-import dotenv
-from gradio_client import Client
+
 from audio_recorder_streamlit import audio_recorder
 from bs4 import BeautifulSoup
 from collections import deque
 from datetime import datetime
 from dotenv import load_dotenv
+from gradio_client import Client
 from huggingface_hub import InferenceClient
 from io import BytesIO
-
+from moviepy.editor import VideoFileClip
+from PIL import Image
 from PyPDF2 import PdfReader
 from templates import bot_template, css, user_template
-from xml.etree import ElementTree as ET
-from PIL import Image
 from urllib.parse import quote  # Ensure this import is included
+from xml.etree import ElementTree as ET
 
 import openai
 from openai import OpenAI
 
-import cv2
-from moviepy.editor import VideoFileClip
-import textract
-
 
 
 # 1. Configuration
@@ -467,14 +465,6 @@ def FileSidebar():
     all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 10]  # exclude files with short names
     all_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True)  # sort by filename length which puts similar prompts together - consider making date and time of file optional.
 
-
-
-    # Button to compare files and delete duplicates
-    #if st.button("Compare and Delete Duplicates"):
-    #    compare_and_delete_files(all_files)
-
-        
-
     # ⬇️ Download
     Files1, Files2 = st.sidebar.columns(2)
     with Files1:
@@ -489,7 +479,6 @@ def FileSidebar():
     file_contents=''
     file_name=''
     next_action=''
-        
 
     # Add files 🌐View, 📂Open, ▶️Run, and 🗑Delete per file
     for file in all_files:
@@ -621,7 +610,6 @@ titles = [
     "🏰 Semantic 🧠 Soul 🙌 & Episodic 📜 Essence",
     "🥁🎻 The Music Of AI's Mind 🧠🎭🎉"
 ]
-
 selected_title = random.choice(titles)
 st.markdown(f"**{selected_title}**")
 
@@ -701,21 +689,10 @@ def load_score(key):
  # 🔍Search Glossary 
 @st.cache_resource
 def search_glossary(query): 
-    #for category, terms in roleplaying_glossary.items():
-    #    if query.lower() in (term.lower() for term in terms):
-    #        st.markdown(f"#### {category}")
-    #        st.write(f"- {query}")
     all=""
     st.markdown(f"- {query}")
     
-
-    # 🔍Run 1 - plain query
-    #response = chat_with_model(query)  
-    #response1 = chat_with_model45(query) 
-    #all = query + '   ' + response1
-    #st.write('🔍Run 1 is Complete.')
-
-    # ArXiv searcher ~-<>-~ Paper Summary - Ask LLM
+    # 🔍Run 1 - ArXiv RAG researcher expert ~-<>-~ Paper Summary & Ask LLM
     client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
     response2 = client.predict(
             query,	# str  in 'parameter_13' Textbox component
@@ -738,42 +715,11 @@ def search_glossary(query):
     		api_name="/update_with_rag_md"
     )
     st.write('🔍Run of Multi-Agent System Paper References is Complete')
-    #st.markdown(response1)
-
     responseall = response2 + response1[0] + response1[1]
     st.markdown(responseall)
     return responseall
 
-# GPT 35 turbo and GPT 45 - - - - - - - - - - - - -<><><><><>:
-    RunPostArxivLLM = False
-    if RunPostArxivLLM:
-        # 🔍Run PaperSummarizer
-        PaperSummarizer = '  Create a paper summary as a markdown table with paper links clustering the features writing short markdown emoji outlines to extract three main ideas from each of the ten summaries. For each one create three simple points led by an emoji of the main three steps needed as method step process for implementing the idea as a single app.py streamlit python app.  '
-        response2 = chat_with_model(PaperSummarizer + str(response1))        
-        st.write('🔍Run 3 - Paper Summarizer is Complete.')
-    
-        # 🔍Run AppSpecifier
-        AppSpecifier = '  Design and write a streamlit python code listing and specification that implements each scientific method steps as ten functions keeping specification in a markdown table in the function comments with original paper link to outline the AI pipeline ensemble implementing code as full plan to build.'       
-        response3 = chat_with_model(AppSpecifier + str(response2))        
-        st.write('🔍Run 4 - AppSpecifier is Complete.')
-        
-        # 🔍Run PythonAppCoder
-        PythonAppCoder = '  Complete this streamlit python app implementing the functions in detail using appropriate python libraries and streamlit user interface elements.  Show full code listing for the completed detail app as full code listing with no comments or commentary.  '
-        #result = str(result).replace('\n', ' ').replace('|', '   ')
-    #    response4 = chat_with_model45(PythonAppCoder + str(response3))        
-        response4 = chat_with_model(PythonAppCoder + str(response3))        
-        st.write('🔍Run Python AppCoder is Complete.')
-    
-    # experimental 45 - - - - - - - - - - - - -<><><><><>
-        
-        responseAll = '# Query: ' + query  + '# Summary: ' +  str(response2)  + '# Streamlit App Specifier: ' +  str(response3)  + '# Complete Streamlit App: ' +  str(response4) + '# Scholarly Article Links References: ' + str(response1)
-        filename = generate_filename(responseAll, "md")
-        create_file(filename, query, responseAll, should_save)
-    
-        return responseAll    # 🔍Run--------------------------------------------------------
-    else:
-        return response1
-    
+
 # Function to display the glossary in a structured format
 def display_glossary(glossary, area):
     if area in glossary: