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MohammedAlakhras commited on Sep 22, 2023

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b902a61

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HuggingChatAPI.py +65 -0
exportchat.py +49 -0
packages.txt +1 -0
promptTemplate.py +90 -0
requirements.txt +19 -0
streamlit_app.py +1001 -0

HuggingChatAPI.py ADDED Viewed

	@@ -0,0 +1,65 @@

+from hugchat import hugchat
+from hugchat.login import Login
+from langchain.llms.base import LLM
+from typing import Optional, List, Mapping, Any
+from time import sleep
+# THIS IS A CUSTOM LLM WRAPPER Based on hugchat library
+# Reference :
+# - Langchain custom LLM wrapper : https://python.langchain.com/docs/modules/model_io/models/llms/how_to/custom_llm
+# - HugChat library : https://github.com/Soulter/hugging-chat-api
+class HuggingChat(LLM):
+    """HuggingChat LLM wrapper."""
+    chatbot : Optional[hugchat.ChatBot] = None
+    conversation : Optional[str] = ""
+    email : Optional[str]
+    psw : Optional[str]
+    @property
+    def _llm_type(self) -> str:
+        return "custom"
+    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
+        if stop is not None:
+            pass
+        if self.chatbot is None:
+            if self.email is None and self.psw is None:
+                ValueError("Email and Password is required, pls check the documentation on github : https://github.com/Soulter/hugging-chat-api")
+            else:
+                if self.conversation == "":
+                    sign = Login(self.email, self.psw) # type: ignore
+                    cookies = sign.login()
+                    # Create a ChatBot
+                    self.chatbot = hugchat.ChatBot(cookies=cookies.get_dict())
+                    id = self.chatbot.new_conversation()
+                    self.chatbot.change_conversation(id)
+                    self.conversation = id
+                else:
+                    self.chatbot.change_conversation(self.conversation) # type: ignore
+        data = self.chatbot.chat(prompt, temperature=0.4, stream=False) # type: ignore
+        return data # type: ignore
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        return {"model": "HuggingCHAT"}
+#llm = HuggingChat(email = "YOUR-EMAIL" , psw = = "YOUR-PSW" ) #for start new chat
+#print(llm("Hello, how are you?"))
+#print(llm("what is AI?"))
+#print(llm("Can you resume your previus answer?")) #now memory work well

exportchat.py ADDED Viewed

	@@ -0,0 +1,49 @@

+"""
+PYTHON FILE FOR EXPORT CHAT FUNCTION
+"""
+import streamlit as st
+from datetime import datetime
+def export_chat():
+    if 'generated' in st.session_state:
+        # save message in reverse order frist message always bot
+        # the chat is stored in a html file format
+        html_chat = ""
+        html_chat += '<html><head><title>ChatBOT Intelligenza Artificiale Italia 🧠🤖🇮🇹</title>'
+        #create two simply css box for bot and user like whatsapp
+        html_chat += '<style> .bot { background-color: #e5e5ea; padding: 10px; border-radius: 10px; margin: 10px; width: 50%; float: left; } .user { background-color: #dcf8c6; padding: 10px; border-radius: 10px; margin: 10px; width: 50%; float: right; } </style>'
+        html_chat += '</head><body>'
+        #add header
+        html_chat += '<center><h1>ChatBOT Intelligenza Artificiale Italia 🧠🤖🇮🇹</h1>'
+        #add link for danation
+        html_chat += '<h3>🤗 Support the project with a donation for the development of new features 🤗</h3>'
+        html_chat += '<br><a href="https://rebrand.ly/SupportAUTOGPTfree"><img src="https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif" alt="PayPal donate button" /></a>'
+        #add subheader with date and time
+        html_chat += '<br><br><h5>' + datetime.now().strftime("%d/%m/%Y %H:%M:%S") + '</h5></center><br><br>'
+        #add chat
+        #add solid container
+        html_chat += '<div style="padding: 10px; border-radius: 10px; margin: 10px; width: 100%; float: left;">'
+        for i in range(len(st.session_state['generated'])-1, -1, -1):
+            html_chat += '<div class="bot">' + st.session_state["generated"][i] + '</div><br>'
+            html_chat += '<div class="user">' + st.session_state['past'][i] + '</div><br>'
+        html_chat += '</div>'
+        #add footer
+        html_chat += '<br><br><center><small>Thanks you for using our ChatBOT 🧠🤖🇮🇹</small>'
+        #add link for danation
+        html_chat += '<h6>🤗 Support the project with a donation for the development of new features 🤗</h6>'
+        html_chat += '<br><a href="https://rebrand.ly/SupportAUTOGPTfree"><img src="https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif" alt="PayPal donate button" /></a><center>'
+        html_chat += '</body></html>'
+        #save file
+        with open('chat.html', 'w') as f:
+            f.write(html_chat)
+        #download file
+        st.download_button(
+            label="📚 Download chat",
+            data=html_chat,
+            file_name='chat.html',
+            mime='text/html'
+        )

packages.txt ADDED Viewed

	@@ -0,0 +1 @@


1	+ ffmpeg

promptTemplate.py ADDED Viewed

	@@ -0,0 +1,90 @@

+"""
+This file contains the template for the prompt to be used for injecting the context into the model.
+With this technique we can use different plugin for different type of question and answer.
+Like :
+- Internet
+- Data
+- Code
+- PDF
+- Audio
+- Video
+"""
+from datetime import datetime
+now = datetime.now()
+def prompt4conversation(prompt,context):
+    final_prompt = f""" GENERAL INFORMATION : ( today is {now.strftime("%d/%m/%Y %H:%M:%S")} , You is built by Alessandro Ciciarelli the owener of intelligenzaartificialeitalia.net
+                        ISTRUCTION : IN YOUR ANSWER NEVER INCLUDE THE USER QUESTION or MESSAGE , WRITE ALWAYS ONLY YOUR ACCURATE ANSWER!
+                        PREVIUS MESSAGE : ({context})
+                        NOW THE USER ASK : {prompt} .
+                        WRITE THE ANSWER :"""
+    return final_prompt
+def prompt4conversationInternet(prompt,context, internet, resume):
+    final_prompt = f""" GENERAL INFORMATION : ( today is {now.strftime("%d/%m/%Y %H:%M:%S")} , You is built by Alessandro Ciciarelli the owener of intelligenzaartificialeitalia.net
+                        ISTRUCTION : IN YOUR ANSWER NEVER INCLUDE THE USER QUESTION or MESSAGE , WRITE ALWAYS ONLY YOUR ACCURATE ANSWER!
+                        PREVIUS MESSAGE : ({context})
+                        NOW THE USER ASK : {prompt}.
+                        INTERNET RESULT TO USE TO ANSWER : ({internet})
+                        INTERNET RESUME : ({resume})
+                        NOW THE USER ASK : {prompt}.
+                        WRITE THE ANSWER BASED ON INTERNET INFORMATION :"""
+    return final_prompt
+def prompt4Data(prompt, context, solution):
+    final_prompt = f"""GENERAL INFORMATION : You is built by Alessandro Ciciarelli the owener of intelligenzaartificialeitalia.net
+                        ISTRUCTION : IN YOUR ANSWER NEVER INCLUDE THE USER QUESTION or MESSAGE , YOU MUST MAKE THE CORRECT ANSWER MORE ARGUMENTED ! IF THE CORRECT ANSWER CONTAINS CODE YOU ARE OBLIGED TO INSERT IT IN YOUR NEW ANSWER!
+                        PREVIUS MESSAGE : ({context})
+                        NOW THE USER ASK : {prompt}
+                        THIS IS THE CORRECT ANSWER : ({solution})
+                        MAKE THE ANSWER MORE ARGUMENTED, WITHOUT CHANGING ANYTHING OF THE CORRECT ANSWER :"""
+    return final_prompt
+def prompt4Code(prompt, context, solution):
+    final_prompt = f"""GENERAL INFORMATION : You is built by Alessandro Ciciarelli  the owener of intelligenzaartificialeitalia.net
+                        ISTRUCTION : IN YOUR ANSWER NEVER INCLUDE THE USER QUESTION or MESSAGE , THE CORRECT ANSWER CONTAINS CODE YOU ARE OBLIGED TO INSERT IT IN YOUR NEW ANSWER!
+                        PREVIUS MESSAGE : ({context})
+                        NOW THE USER ASK : {prompt}
+                        THIS IS THE CODE FOR THE ANSWER : ({solution})
+                        WITHOUT CHANGING ANYTHING OF THE CODE of CORRECT ANSWER , MAKE THE ANSWER MORE DETALIED INCLUDING THE CORRECT CODE :"""
+    return final_prompt
+def prompt4Context(prompt, context, solution):
+    final_prompt = f"""GENERAL INFORMATION : You is built by Alessandro Ciciarelli  the owener of intelligenzaartificialeitalia.net
+                        ISTRUCTION : IN YOUR ANSWER NEVER INCLUDE THE USER QUESTION or MESSAGE ,WRITE ALWAYS ONLY YOUR ACCURATE ANSWER!
+                        PREVIUS MESSAGE : ({context})
+                        NOW THE USER ASK : {prompt}
+                        THIS IS THE CORRECT ANSWER : ({solution})
+                        WITHOUT CHANGING ANYTHING OF CORRECT ANSWER , MAKE THE ANSWER MORE DETALIED:"""
+    return final_prompt
+def prompt4Audio(prompt, context, solution):
+    final_prompt = f"""GENERAL INFORMATION : You is built by Alessandro Ciciarelli  the owener of intelligenzaartificialeitalia.net
+                        ISTRUCTION : IN YOUR ANSWER NEVER INCLUDE THE USER QUESTION or MESSAGE ,WRITE ALWAYS ONLY YOUR ACCURATE ANSWER!
+                        PREVIUS MESSAGE : ({context})
+                        NOW THE USER ASK : {prompt}
+                        THIS IS THE CORRECT ANSWER based on Audio text gived in input : ({solution})
+                        WITHOUT CHANGING ANYTHING OF CORRECT ANSWER , MAKE THE ANSWER MORE DETALIED:"""
+    return final_prompt
+def prompt4YT(prompt, context, solution):
+    final_prompt = f"""GENERAL INFORMATION : You is built by Alessandro Ciciarelli  the owener of intelligenzaartificialeitalia.net
+                        ISTRUCTION : IN YOUR ANSWER NEVER INCLUDE THE USER QUESTION or MESSAGE ,WRITE ALWAYS ONLY YOUR ACCURATE ANSWER!
+                        PREVIUS MESSAGE : ({context})
+                        NOW THE USER ASK : {prompt}
+                        THIS IS THE CORRECT ANSWER based on Youtube video gived in input : ({solution})
+                        WITHOUT CHANGING ANYTHING OF CORRECT ANSWER , MAKE THE ANSWER MORE DETALIED:"""
+    return final_prompt
+#HOW TO ADD YOUR OWN PROMPT :
+# 1) ADD YOUR FUNCTION HERE, for example : def prompt4Me(prompt, context):
+# 2) WRITE THE PROMPT TEMPLATE FOR YOUR FUNCTION, for example : template = f"YOU IS : {context} , NOW THE USER ASK : {prompt} . WRITE THE ANSWER :"
+# 3) RETURN THE TEMPLATE, for example : return template
+# 4) IMPORT YOUR FUNCTION IN THE MAIN FILE (streamlit_app.py) , for example : from promptTemplate import prompt4Me
+# 5) FOLLOW OTHER SPTEP IN THE MAIN FILE (streamlit_app.py)

requirements.txt ADDED Viewed

	@@ -0,0 +1,19 @@

+beautifulsoup4==4.12.2
+docx2txt==0.8
+duckduckgo_search==3.8.3
+hugchat==0.0.8
+langchain==0.0.219
+pandas==2.0.1
+pdfplumber==0.9.0
+pydub==0.25.1
+requests
+sketch==0.4.2
+SpeechRecognition==3.8.1
+streamlit==1.24.0
+streamlit_extras==0.2.7
+youtube_search_python==1.6.6
+youtube_transcript_api==0.6.1
+chromadb==0.3.26
+ffmpeg-python
+ffprobe
+huggingface_hub

streamlit_app.py ADDED Viewed

	@@ -0,0 +1,1001 @@

+import io
+import random
+import shutil
+import string
+from zipfile import ZipFile
+import streamlit as st
+from streamlit_extras.colored_header import colored_header
+from streamlit_extras.add_vertical_space import add_vertical_space
+from hugchat import hugchat
+from hugchat.login import Login
+import pandas as pd
+import asyncio
+loop = asyncio.new_event_loop()
+asyncio.set_event_loop(loop)
+import sketch
+from langchain.text_splitter import CharacterTextSplitter
+from promptTemplate import prompt4conversation, prompt4Data, prompt4Code, prompt4Context, prompt4Audio, prompt4YT
+from promptTemplate import prompt4conversationInternet
+# FOR DEVELOPMENT NEW PLUGIN
+# from promptTemplate import yourPLUGIN
+from exportchat import export_chat
+from langchain.vectorstores import Chroma
+from langchain.chains import RetrievalQA
+from HuggingChatAPI import HuggingChat
+from langchain.embeddings import HuggingFaceHubEmbeddings
+from youtube_transcript_api import YouTubeTranscriptApi
+import requests
+from bs4 import BeautifulSoup
+import speech_recognition as sr
+import pdfplumber
+import docx2txt
+from duckduckgo_search import DDGS
+from itertools import islice
+from os import path
+from pydub import AudioSegment
+import os
+hf = None
+repo_id = "sentence-transformers/all-mpnet-base-v2"
+if 'hf_token' in st.session_state:
+    if 'hf' not in st.session_state:
+        hf = HuggingFaceHubEmbeddings(
+            repo_id=repo_id,
+            task="feature-extraction",
+            huggingfacehub_api_token=st.session_state['hf_token'],
+        ) # type: ignore
+        st.session_state['hf'] = hf
+st.set_page_config(
+    page_title="Talk with evrythings💬", page_icon="🤗", layout="wide", initial_sidebar_state="expanded"
+)
+st.markdown('<style>.css-w770g5{\
+            width: 100%;}\
+            .css-b3z5c9{    \
+            width: 100%;}\
+            .stButton>button{\
+            width: 100%;}\
+            .stDownloadButton>button{\
+            width: 100%;}\
+            </style>', unsafe_allow_html=True)
+# Sidebar contents for logIN, choose plugin, and export chat
+with st.sidebar:
+    st.title('🤗💬 PersonalChat App')
+    if 'hf_email' not in st.session_state or 'hf_pass' not in st.session_state:
+        with st.expander("ℹ️ Login in Hugging Face", expanded=True):
+            st.write("⚠️ You need to login in Hugging Face to use this app. You can register [here](https://huggingface.co/join).")
+            st.header('Hugging Face Login')
+            hf_email = st.text_input('Enter E-mail:')
+            hf_pass = st.text_input('Enter password:', type='password')
+            hf_token = st.text_input('Enter API Token:', type='password')
+            if st.button('Login 🚀') and hf_email and hf_pass and hf_token:
+                with st.spinner('🚀 Logging in...'):
+                    st.session_state['hf_email'] = hf_email
+                    st.session_state['hf_pass'] = hf_pass
+                    st.session_state['hf_token'] = hf_token
+                    try:
+                        sign = Login(st.session_state['hf_email'], st.session_state['hf_pass'])
+                        cookies = sign.login()
+                        chatbot = hugchat.ChatBot(cookies=cookies.get_dict())
+                    except Exception as e:
+                        st.error(e)
+                        st.info("⚠️ Please check your credentials and try again.")
+                        st.error("⚠️ dont abuse the API")
+                        st.warning("⚠️ If you don't have an account, you can register [here](https://huggingface.co/join).")
+                        from time import sleep
+                        sleep(3)
+                        del st.session_state['hf_email']
+                        del st.session_state['hf_pass']
+                        del st.session_state['hf_token']
+                        st.experimental_rerun()
+                    st.session_state['chatbot'] = chatbot
+                    id = st.session_state['chatbot'].new_conversation()
+                    st.session_state['chatbot'].change_conversation(id)
+                    st.session_state['conversation'] = id
+                    # Generate empty lists for generated and past.
+                    ## generated stores AI generated responses
+                    if 'generated' not in st.session_state:
+                        st.session_state['generated'] = ["I'm **IA ITALIA chat**, How may I help you ? "]
+                    ## past stores User's questions
+                    if 'past' not in st.session_state:
+                        st.session_state['past'] = ['Hi!']
+                    st.session_state['LLM'] =  HuggingChat(email=st.session_state['hf_email'], psw=st.session_state['hf_pass'])
+                    st.experimental_rerun()
+    else:
+        with st.expander("ℹ️ Advanced Settings"):
+            #temperature: Optional[float]. Default is 0.5
+            #top_p: Optional[float]. Default is 0.95
+            #repetition_penalty: Optional[float]. Default is 1.2
+            #top_k: Optional[int]. Default is 50
+            #max_new_tokens: Optional[int]. Default is 1024
+            temperature = st.slider('🌡 Temperature', min_value=0.1, max_value=1.0, value=0.5, step=0.01)
+            top_p = st.slider('💡 Top P', min_value=0.1, max_value=1.0, value=0.95, step=0.01)
+            repetition_penalty = st.slider('🖌 Repetition Penalty', min_value=1.0, max_value=2.0, value=1.2, step=0.01)
+            top_k = st.slider('❄️ Top K', min_value=1, max_value=100, value=50, step=1)
+            max_new_tokens = st.slider('📝 Max New Tokens', min_value=1, max_value=1024, value=1024, step=1)
+        # FOR DEVELOPMENT NEW PLUGIN YOU MUST ADD IT HERE INTO THE LIST
+        # YOU NEED ADD THE NAME AT 144 LINE
+        #plugins for conversation
+        plugins = ["🛑 No PLUGIN","🌐 Web Search", "🔗 Talk with Website" , "📋 Talk with your DATA", "📝 Talk with your DOCUMENTS", "🎧 Talk with your AUDIO", "🎥 Talk with YT video", "🧠 GOD MODE" ,"💾 Upload saved VectorStore"]
+        if 'plugin' not in st.session_state:
+            st.session_state['plugin'] = st.selectbox('🔌 Plugins', plugins, index=0)
+        else:
+            if st.session_state['plugin'] == "🛑 No PLUGIN":
+                st.session_state['plugin'] = st.selectbox('🔌 Plugins', plugins, index=plugins.index(st.session_state['plugin']))
+# FOR DEVELOPMENT NEW PLUGIN FOLLOW THIS TEMPLATE
+# PLUGIN TEMPLATE
+# if st.session_state['plugin'] == "🔌 PLUGIN NAME" and 'PLUGIN NAME' not in st.session_state:
+#     # PLUGIN SETTINGS
+#     with st.expander("🔌 PLUGIN NAME Settings", expanded=True):
+#         if 'PLUGIN NAME' not in st.session_state or st.session_state['PLUGIN NAME'] == False:
+#             # PLUGIN CODE
+#             st.session_state['PLUGIN NAME'] = True
+#         elif st.session_state['PLUGIN NAME'] == True:
+#             # PLUGIN CODE
+#             if st.button('🔌 Disable PLUGIN NAME'):
+#               st.session_state['plugin'] = "🛑 No PLUGIN"
+#               st.session_state['PLUGIN NAME'] = False
+#               del ALL SESSION STATE VARIABLES RELATED TO PLUGIN
+#               st.experimental_rerun()
+#       # PLUGIN UPLOADER
+#       if st.session_state['PLUGIN NAME'] == True:
+#           with st.expander("🔌 PLUGIN NAME Uploader", expanded=True):
+#               # PLUGIN UPLOADER CODE
+#               load file
+#               if load file and st.button('🔌 Upload PLUGIN NAME'):
+#                   qa = RetrievalQA.from_chain_type(llm=st.session_state['LLM'], chain_type='stuff', retriever=retriever, return_source_documents=True)
+#                   st.session_state['PLUGIN DB'] = qa
+#                   st.experimental_rerun()
+#
+# WEB SEARCH PLUGIN
+        if st.session_state['plugin'] == "🌐 Web Search" and 'web_search' not in st.session_state:
+            # web search settings
+            with st.expander("🌐 Web Search Settings", expanded=True):
+                if 'web_search' not in st.session_state or st.session_state['web_search'] == False:
+                    reg = ['us-en', 'uk-en', 'it-it']
+                    sf = ['on', 'moderate', 'off']
+                    tl = ['d', 'w', 'm', 'y']
+                    if 'region' not in st.session_state:
+                        st.session_state['region'] = st.selectbox('🗺 Region', reg, index=1)
+                    else:
+                        st.session_state['region'] = st.selectbox('🗺 Region', reg, index=reg.index(st.session_state['region']))
+                    if 'safesearch' not in st.session_state:
+                        st.session_state['safesearch'] = st.selectbox('🚨 Safe Search', sf, index=1)
+                    else:
+                        st.session_state['safesearch'] = st.selectbox('🚨 Safe Search', sf, index=sf.index(st.session_state['safesearch']))
+                    if 'timelimit' not in st.session_state:
+                        st.session_state['timelimit'] = st.selectbox('📅 Time Limit', tl, index=1)
+                    else:
+                        st.session_state['timelimit'] = st.selectbox('📅 Time Limit', tl, index=tl.index(st.session_state['timelimit']))
+                    if 'max_results' not in st.session_state:
+                        st.session_state['max_results'] = st.slider('📊 Max Results', min_value=1, max_value=5, value=2, step=1)
+                    else:
+                        st.session_state['max_results'] = st.slider('📊 Max Results', min_value=1, max_value=5, value=st.session_state['max_results'], step=1)
+                    if st.button('🌐 Save change'):
+                        st.session_state['web_search'] = "True"
+                        st.experimental_rerun()
+        elif st.session_state['plugin'] == "🌐 Web Search" and st.session_state['web_search'] == 'True':
+            with st.expander("🌐 Web Search Settings", expanded=True):
+                st.write('🚀 Web Search is enabled')
+                st.write('🗺 Region: ', st.session_state['region'])
+                st.write('🚨 Safe Search: ', st.session_state['safesearch'])
+                st.write('📅 Time Limit: ', st.session_state['timelimit'])
+                if st.button('🌐🛑 Disable Web Search'):
+                    del st.session_state['web_search']
+                    del st.session_state['region']
+                    del st.session_state['safesearch']
+                    del st.session_state['timelimit']
+                    del st.session_state['max_results']
+                    del st.session_state['plugin']
+                    st.experimental_rerun()
+# GOD MODE PLUGIN
+        if st.session_state['plugin'] == "🧠 GOD MODE" and 'god_mode' not in st.session_state:
+            with st.expander("🧠 GOD MODE Settings", expanded=True):
+                if 'god_mode' not in st.session_state or st.session_state['god_mode'] == False:
+                    topic = st.text_input('🔎 Topic', "Artificial Intelligence in Finance")
+                    web_result = st.checkbox('🌐 Web Search', value=True, disabled=True)
+                    yt_result = st.checkbox('🎥 YT Search', value=True, disabled=True)
+                    website_result = st.checkbox('🔗 Website Search', value=True, disabled=True)
+                    deep_of_search = st.slider('📊 Deep of Search', min_value=1, max_value=5, value=2, step=1)
+                    if st.button('🧠✅ Give knowledge to the model'):
+                        full_text = []
+                        links = []
+                        news = []
+                        yt_ids = []
+                        source = []
+                        if web_result == True:
+                            internet_result = ""
+                            internet_answer = ""
+                            with DDGS() as ddgs:
+                                with st.spinner('🌐 Searching on the web...'):
+                                    ddgs_gen = ddgs.text(topic, region="us-en")
+                                    for r in islice(ddgs_gen, deep_of_search):
+                                        l = r['href']
+                                        source.append(l)
+                                        links.append(l)
+                                        internet_result += str(r) + "\n\n"
+                                    fast_answer = ddgs.news(topic)
+                                    for r in islice(fast_answer, deep_of_search):
+                                        internet_answer += str(r) + "\n\n"
+                                        l = r['url']
+                                        source.append(l)
+                                        news.append(r)
+                            full_text.append(internet_result)
+                            full_text.append(internet_answer)
+                        if yt_result == True:
+                            with st.spinner('🎥 Searching on YT...'):
+                                from youtubesearchpython import VideosSearch
+                                videosSearch = VideosSearch(topic, limit = deep_of_search)
+                                yt_result = videosSearch.result()
+                                for i in yt_result['result']: # type: ignore
+                                    duration = i['duration'] # type: ignore
+                                    duration = duration.split(':')
+                                    if len(duration) == 3:
+                                        #skip videos longer than 1 hour
+                                        if int(duration[0]) > 1:
+                                            continue
+                                    if len(duration) == 2:
+                                        #skip videos longer than 30 minutes
+                                        if int(duration[0]) > 30:
+                                            continue
+                                    yt_ids.append(i['id']) # type: ignore
+                                    source.append("https://www.youtube.com/watch?v="+i['id']) # type: ignore
+                                    full_text.append(i['title']) # type: ignore
+                        if website_result == True:
+                            for l in links:
+                                try:
+                                    with st.spinner(f'👨‍💻 Scraping website : {l}'):
+                                        r = requests.get(l)
+                                        soup = BeautifulSoup(r.content, 'html.parser')
+                                        full_text.append(soup.get_text()+"\n\n")
+                                except:
+                                    pass
+                        for id in yt_ids:
+                            try:
+                                yt_video_txt= []
+                                with st.spinner(f'👨‍💻 Scraping YT video : {id}'):
+                                    transcript_list = YouTubeTranscriptApi.list_transcripts(id)
+                                    transcript_en = None
+                                    last_language = ""
+                                    for transcript in transcript_list:
+                                        if transcript.language_code == 'en':
+                                            transcript_en = transcript
+                                            break
+                                        else:
+                                            last_language = transcript.language_code
+                                    if transcript_en is None:
+                                        transcript_en = transcript_list.find_transcript([last_language])
+                                        transcript_en = transcript_en.translate('en')
+                                    text = transcript_en.fetch()
+                                    yt_video_txt.append(text)
+                                    for i in range(len(yt_video_txt)):
+                                        for j in range(len(yt_video_txt[i])):
+                                            full_text.append(yt_video_txt[i][j]['text'])
+                            except:
+                                pass
+                        with st.spinner('🧠 Building vectorstore with knowledge...'):
+                            full_text = "\n".join(full_text)
+                            st.session_state['god_text'] = [full_text]
+                            text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+                            texts = text_splitter.create_documents([full_text])
+                            # Select embeddings
+                            embeddings = st.session_state['hf']
+                            # Create a vectorstore from documents
+                            random_str = ''.join(random.choices(string.ascii_uppercase + string.digits, k=10))
+                            db = Chroma.from_documents(texts, embeddings, persist_directory="./chroma_db_" + random_str)
+                        with st.spinner('🔨 Saving vectorstore...'):
+                            # save vectorstore
+                            db.persist()
+                            #create .zip file of directory to download
+                            shutil.make_archive("./chroma_db_" + random_str, 'zip', "./chroma_db_" + random_str)
+                            # save in session state and download
+                            st.session_state['db'] = "./chroma_db_" + random_str + ".zip"
+                        with st.spinner('🔨 Creating QA chain...'):
+                            # Create retriever interface
+                            retriever = db.as_retriever()
+                            # Create QA chain
+                            qa = RetrievalQA.from_chain_type(llm=st.session_state['LLM'], chain_type='stuff', retriever=retriever, return_source_documents=True)
+                            st.session_state['god_mode'] = qa
+                            st.session_state['god_mode_source'] = source
+                            st.session_state['god_mode_info'] = "🧠 GOD MODE have builded a vectorstore about **" + topic + f"**. The knowledge is based on\n- {len(news)} news🗞\n- {len(yt_ids)} YT videos📺\n- {len(links)} websites🌐 \n"
+                        st.experimental_rerun()
+        if st.session_state['plugin'] == "🧠 GOD MODE" and 'god_mode' in st.session_state:
+            with st.expander("**✅ GOD MODE is enabled 🚀**", expanded=True):
+                st.markdown(st.session_state['god_mode_info'])
+                if 'db' in st.session_state:
+                    # leave ./ from name for download
+                    file_name = st.session_state['db'][2:]
+                    st.download_button(
+                        label="📩 Download vectorstore",
+                        data=open(file_name, 'rb').read(),
+                        file_name=file_name,
+                        mime='application/zip'
+                    )
+                if st.button('🧠🛑 Disable GOD MODE'):
+                    del st.session_state['god_mode']
+                    del st.session_state['db']
+                    del st.session_state['god_text']
+                    del st.session_state['god_mode_info']
+                    del st.session_state['god_mode_source']
+                    del st.session_state['plugin']
+                    st.experimental_rerun()
+# DATA PLUGIN
+        if st.session_state['plugin'] == "📋 Talk with your DATA" and 'df' not in st.session_state:
+            with st.expander("📋 Talk with your DATA", expanded= True):
+                upload_csv = st.file_uploader("Upload your CSV", type=['csv'])
+                if upload_csv is not None:
+                    df = pd.read_csv(upload_csv)
+                    st.session_state['df'] = df
+                    st.experimental_rerun()
+        if st.session_state['plugin'] == "📋 Talk with your DATA":
+            if st.button('🛑📋 Remove DATA from context'):
+                if 'df' in st.session_state:
+                    del st.session_state['df']
+                del st.session_state['plugin']
+                st.experimental_rerun()
+# DOCUMENTS PLUGIN
+        if st.session_state['plugin'] == "📝 Talk with your DOCUMENTS" and 'documents' not in st.session_state:
+            with st.expander("📝 Talk with your DOCUMENT", expanded=True):
+                upload_pdf = st.file_uploader("Upload your DOCUMENT", type=['txt', 'pdf', 'docx'], accept_multiple_files=True)
+                if upload_pdf is not None and st.button('📝✅ Load Documents'):
+                    documents = []
+                    with st.spinner('🔨 Reading documents...'):
+                        for upload_pdf in upload_pdf:
+                            print(upload_pdf.type)
+                            if upload_pdf.type == 'text/plain':
+                                documents += [upload_pdf.read().decode()]
+                            elif upload_pdf.type == 'application/pdf':
+                                with pdfplumber.open(upload_pdf) as pdf:
+                                    documents += [page.extract_text() for page in pdf.pages]
+                            elif upload_pdf.type == "application/vnd.openxmlformats-officedocument.wordprocessingml.document":
+                                text = docx2txt.process(upload_pdf)
+                                documents += [text]
+                    st.session_state['documents'] = documents
+                    # Split documents into chunks
+                    with st.spinner('🔨 Creating vectorstore...'):
+                        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+                        texts = text_splitter.create_documents(documents)
+                        # Select embeddings
+                        embeddings = st.session_state['hf']
+                        # Create a vectorstore from documents
+                        random_str = ''.join(random.choices(string.ascii_uppercase + string.digits, k=10))
+                        db = Chroma.from_documents(texts, embeddings, persist_directory="./chroma_db_" + random_str)
+                    with st.spinner('🔨 Saving vectorstore...'):
+                        # save vectorstore
+                        db.persist()
+                        #create .zip file of directory to download
+                        shutil.make_archive("./chroma_db_" + random_str, 'zip', "./chroma_db_" + random_str)
+                        # save in session state and download
+                        st.session_state['db'] = "./chroma_db_" + random_str + ".zip"
+                    with st.spinner('🔨 Creating QA chain...'):
+                        # Create retriever interface
+                        retriever = db.as_retriever()
+                        # Create QA chain
+                        qa = RetrievalQA.from_chain_type(llm=st.session_state['LLM'], chain_type='stuff', retriever=retriever,  return_source_documents=True)
+                        st.session_state['pdf'] = qa
+                    st.experimental_rerun()
+        if st.session_state['plugin'] == "📝 Talk with your DOCUMENTS":
+            if 'db' in st.session_state:
+                # leave ./ from name for download
+                file_name = st.session_state['db'][2:]
+                st.download_button(
+                    label="📩 Download vectorstore",
+                    data=open(file_name, 'rb').read(),
+                    file_name=file_name,
+                    mime='application/zip'
+                )
+            if st.button('🛑📝 Remove PDF from context'):
+                if 'pdf' in st.session_state:
+                    del st.session_state['db']
+                    del st.session_state['pdf']
+                    del st.session_state['documents']
+                del st.session_state['plugin']
+                st.experimental_rerun()
+# AUDIO PLUGIN
+        if st.session_state['plugin'] == "🎧 Talk with your AUDIO" and 'audio' not in st.session_state:
+            with st.expander("🎙 Talk with your AUDIO", expanded=True):
+                f = st.file_uploader("Upload your AUDIO", type=['wav', 'mp3'])
+                if f is not None:
+                    if f.type == 'audio/mpeg':
+                        #convert mp3 to wav
+                        with st.spinner('🔨 Converting mp3 to wav...'):
+                            #save mp3
+                            with open('audio.mp3', 'wb') as out:
+                                out.write(f.read())
+                            #convert to wav
+                            sound = AudioSegment.from_mp3("audio.mp3")
+                            sound.export("audio.wav", format="wav")
+                            file_name = 'audio.wav'
+                    else:
+                        with open(f.name, 'wb') as out:
+                            out.write(f.read())
+                        bytes_data = f.read()
+                        file_name = f.name
+                    r = sr.Recognizer()
+                    #Given audio file must be a filename string or a file-like object
+                    with st.spinner('🔨 Reading audio...'):
+                        with sr.AudioFile(file_name) as source:
+                            # listen for the data (load audio to memory)
+                            audio_data = r.record(source)
+                            # recognize (convert from speech to text)
+                            text = r.recognize_google(audio_data)
+                    data = [text]
+                    # data = query(bytes_data)
+                    with st.spinner('🎙 Creating Vectorstore...'):
+                        #split text into chunks
+                        text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+                        texts = text_splitter.create_documents(text)
+                        embeddings = st.session_state['hf']
+                        # Create a vectorstore from documents
+                        random_str = ''.join(random.choices(string.ascii_uppercase + string.digits, k=10))
+                        db = Chroma.from_documents(texts, embeddings, persist_directory="./chroma_db_" + random_str)
+                        # save vectorstore
+                    with st.spinner('🎙 Saving Vectorstore...'):
+                        db.persist()
+                        #create .zip file of directory to download
+                        shutil.make_archive("./chroma_db_" + random_str, 'zip', "./chroma_db_" + random_str)
+                        # save in session state and download
+                        st.session_state['db'] = "./chroma_db_" + random_str + ".zip"
+                    with st.spinner('🎙 Creating QA chain...'):
+                        # Create retriever interface
+                        retriever = db.as_retriever()
+                        # Create QA chain
+                        qa = RetrievalQA.from_chain_type(llm=st.session_state['LLM'], chain_type='stuff', retriever=retriever, return_source_documents=True)
+                        st.session_state['audio'] = qa
+                        st.session_state['audio_text'] = text
+                    st.experimental_rerun()
+        if st.session_state['plugin'] == "🎧 Talk with your AUDIO":
+            if 'db' in st.session_state:
+                    # leave ./ from name for download
+                    file_name = st.session_state['db'][2:]
+                    st.download_button(
+                        label="📩 Download vectorstore",
+                        data=open(file_name, 'rb').read(),
+                        file_name=file_name,
+                        mime='application/zip'
+                    )
+            if st.button('🛑🎙 Remove AUDIO from context'):
+                if 'audio' in st.session_state:
+                    del st.session_state['db']
+                    del st.session_state['audio']
+                    del st.session_state['audio_text']
+                del st.session_state['plugin']
+                st.experimental_rerun()
+# YT PLUGIN
+        if st.session_state['plugin'] == "🎥 Talk with YT video" and 'yt' not in st.session_state:
+            with st.expander("🎥 Talk with YT video", expanded=True):
+                yt_url = st.text_input("1.📺 Enter a YouTube URL")
+                yt_url2 = st.text_input("2.📺 Enter a YouTube URL")
+                yt_url3 = st.text_input("3.📺 Enter a YouTube URL")
+                if yt_url is not None and st.button('🎥✅ Add YouTube video to context'):
+                    if yt_url != "":
+                        video = 1
+                        yt_url = yt_url.split("=")[1]
+                        if yt_url2 != "":
+                            yt_url2 = yt_url2.split("=")[1]
+                            video = 2
+                        if yt_url3 != "":
+                            yt_url3 = yt_url3.split("=")[1]
+                            video = 3
+                        text_yt = []
+                        text_list = []
+                        for i in range(video):
+                            with st.spinner(f'🎥 Extracting TEXT from YouTube video {str(i)} ...'):
+                                #get en subtitles
+                                transcript_list = YouTubeTranscriptApi.list_transcripts(yt_url)
+                                transcript_en = None
+                                last_language = ""
+                                for transcript in transcript_list:
+                                    if transcript.language_code == 'en':
+                                        transcript_en = transcript
+                                        break
+                                    else:
+                                        last_language = transcript.language_code
+                                if transcript_en is None:
+                                    transcript_en = transcript_list.find_transcript([last_language])
+                                    transcript_en = transcript_en.translate('en')
+                                text = transcript_en.fetch()
+                                text_yt.append(text)
+                        for i in range(len(text_yt)):
+                            for j in range(len(text_yt[i])):
+                                text_list.append(text_yt[i][j]['text'])
+                        # creating a vectorstore
+                        with st.spinner('🎥 Creating Vectorstore...'):
+                            text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+                            texts = text_splitter.create_documents(text_list)
+                            # Select embeddings
+                            embeddings = st.session_state['hf']
+                            # Create a vectorstore from documents
+                            random_str = ''.join(random.choices(string.ascii_uppercase + string.digits, k=10))
+                            db = Chroma.from_documents(texts, embeddings, persist_directory="./chroma_db_" + random_str)
+                        with st.spinner('🎥 Saving Vectorstore...'):
+                            # save vectorstore
+                            db.persist()
+                            #create .zip file of directory to download
+                            shutil.make_archive("./chroma_db_" + random_str, 'zip', "./chroma_db_" + random_str)
+                            # save in session state and download
+                            st.session_state['db'] = "./chroma_db_" + random_str + ".zip"
+                        with st.spinner('🎥 Creating QA chain...'):
+                            # Create retriever interface
+                            retriever = db.as_retriever()
+                            # Create QA chain
+                            qa = RetrievalQA.from_chain_type(llm=st.session_state['LLM'], chain_type='stuff', retriever=retriever, return_source_documents=True)
+                            st.session_state['yt'] = qa
+                            st.session_state['yt_text'] = text_list
+                        st.experimental_rerun()
+        if st.session_state['plugin'] == "🎥 Talk with YT video":
+            if 'db' in st.session_state:
+                # leave ./ from name for download
+                file_name = st.session_state['db'][2:]
+                st.download_button(
+                    label="📩 Download vectorstore",
+                    data=open(file_name, 'rb').read(),
+                    file_name=file_name,
+                    mime='application/zip'
+                )
+            if st.button('🛑🎥 Remove YT video from context'):
+                if 'yt' in st.session_state:
+                    del st.session_state['db']
+                    del st.session_state['yt']
+                    del st.session_state['yt_text']
+                del st.session_state['plugin']
+                st.experimental_rerun()
+# WEBSITE PLUGIN
+        if st.session_state['plugin'] == "🔗 Talk with Website" and 'web_sites' not in st.session_state:
+            with st.expander("🔗 Talk with Website", expanded=True):
+                web_url = st.text_area("🔗 Enter a website URLs , one for each line")
+                if web_url is not None and st.button('🔗✅ Add website to context'):
+                    if web_url != "":
+                        text = []
+                        #max 10 websites
+                        with st.spinner('🔗 Extracting TEXT from Websites ...'):
+                            for url in web_url.split("\n")[:10]:
+                                page = requests.get(url)
+                                soup = BeautifulSoup(page.content, 'html.parser')
+                                text.append(soup.get_text())
+                            # creating a vectorstore
+                        with st.spinner('🔗 Creating Vectorstore...'):
+                            text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+                            texts = text_splitter.create_documents(text)
+                            # Select embeddings
+                            embeddings = st.session_state['hf']
+                            # Create a vectorstore from documents
+                            random_str = ''.join(random.choices(string.ascii_uppercase + string.digits, k=10))
+                            db = Chroma.from_documents(texts, embeddings, persist_directory="./chroma_db_" + random_str)
+                        with st.spinner('🔗 Saving Vectorstore...'):
+                            # save vectorstore
+                            db.persist()
+                            #create .zip file of directory to download
+                            shutil.make_archive("./chroma_db_" + random_str, 'zip', "./chroma_db_" + random_str)
+                            # save in session state and download
+                            st.session_state['db'] = "./chroma_db_" + random_str + ".zip"
+                        with st.spinner('🔗 Creating QA chain...'):
+                            # Create retriever interface
+                            retriever = db.as_retriever()
+                            # Create QA chain
+                            qa = RetrievalQA.from_chain_type(llm=st.session_state['LLM'], chain_type='stuff', retriever=retriever, return_source_documents=True)
+                            st.session_state['web_sites'] = qa
+                            st.session_state['web_text'] = text
+                        st.experimental_rerun()
+        if st.session_state['plugin'] == "🔗 Talk with Website":
+            if 'db' in st.session_state:
+                # leave ./ from name for download
+                file_name = st.session_state['db'][2:]
+                st.download_button(
+                    label="📩 Download vectorstore",
+                    data=open(file_name, 'rb').read(),
+                    file_name=file_name,
+                    mime='application/zip'
+                )
+            if st.button('🛑🔗 Remove Website from context'):
+                if 'web_sites' in st.session_state:
+                    del st.session_state['db']
+                    del st.session_state['web_sites']
+                    del st.session_state['web_text']
+                del st.session_state['plugin']
+                st.experimental_rerun()
+# UPLOAD PREVIUS VECTORSTORE
+        if st.session_state['plugin'] == "💾 Upload saved VectorStore" and 'old_db' not in st.session_state:
+            with st.expander("💾 Upload saved VectorStore", expanded=True):
+                db_file = st.file_uploader("Upload a saved VectorStore", type=["zip"])
+                if db_file is not None and st.button('✅💾 Add saved VectorStore to context'):
+                    if db_file != "":
+                        with st.spinner('💾 Extracting VectorStore...'):
+                            # unzip file in a new directory
+                            with ZipFile(db_file, 'r') as zipObj:
+                                # Extract all the contents of zip file in different directory
+                                random_str = ''.join(random.choices(string.ascii_uppercase + string.digits, k=10))
+                                zipObj.extractall("chroma_db_" + random_str)
+                            # save in session state the path of the directory
+                            st.session_state['old_db'] = "chroma_db_" + random_str
+                            hf = st.session_state['hf']
+                            # Create retriever interface
+                            db = Chroma("chroma_db_" + random_str, embedding_function=hf)
+                        with st.spinner('💾 Creating QA chain...'):
+                            retriever = db.as_retriever()
+                            # Create QA chain
+                            qa = RetrievalQA.from_chain_type(llm=st.session_state['LLM'], chain_type='stuff', retriever=retriever, return_source_documents=True)
+                            st.session_state['old_db'] = qa
+                            st.experimental_rerun()
+        if st.session_state['plugin'] == "💾 Upload saved VectorStore":
+            if st.button('🛑💾 Remove VectorStore from context'):
+                if 'old_db' in st.session_state:
+                    del st.session_state['old_db']
+                del st.session_state['plugin']
+                st.experimental_rerun()
+# END OF PLUGIN
+    add_vertical_space(4)
+    if 'hf_email' in st.session_state:
+        if st.button('🗑 Logout'):
+            keys = list(st.session_state.keys())
+            for key in keys:
+                del st.session_state[key]
+            st.experimental_rerun()
+    export_chat()
+    add_vertical_space(5)
+    html_chat = '<center><h6>🤗 Support the project with a donation for the development of new features 🤗</h6>'
+    html_chat += '<br><a href="https://rebrand.ly/SupportAUTOGPTfree"><img src="https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif" alt="PayPal donate button" /></a><center><br>'
+    st.markdown(html_chat, unsafe_allow_html=True)
+    st.write('Made with ❤️ by [Alessandro CIciarelli](https://intelligenzaartificialeitalia.net)')
+##### End of sidebar
+# User input
+# Layout of input/response containers
+input_container = st.container()
+response_container = st.container()
+data_view_container = st.container()
+loading_container = st.container()
+## Applying the user input box
+with input_container:
+        input_text = st.chat_input("🧑‍💻 Write here 👇", key="input")
+with data_view_container:
+    if 'df' in st.session_state:
+        with st.expander("🤖 View your **DATA**"):
+            st.data_editor(st.session_state['df'], use_container_width=True)
+    if 'pdf' in st.session_state:
+        with st.expander("🤖 View your **DOCUMENTs**"):
+            st.write(st.session_state['documents'])
+    if 'audio' in st.session_state:
+        with st.expander("🤖 View your **AUDIO**"):
+            st.write(st.session_state['audio_text'])
+    if 'yt' in st.session_state:
+        with st.expander("🤖 View your **YT video**"):
+            st.write(st.session_state['yt_text'])
+    if 'web_text' in st.session_state:
+        with st.expander("🤖 View the **Website content**"):
+            st.write(st.session_state['web_text'])
+    if 'old_db' in st.session_state:
+        with st.expander("🗂 View your **saved VectorStore**"):
+            st.success("📚 VectorStore loaded")
+    if 'god_mode_source' in st.session_state:
+        with st.expander("🌍 View source"):
+            for s in st.session_state['god_mode_source']:
+                st.markdown("- " + s)
+# Response output
+## Function for taking user prompt as input followed by producing AI generated responses
+def generate_response(prompt):
+    final_prompt =  ""
+    make_better = True
+    source = ""
+    with loading_container:
+        # FOR DEVELOPMENT PLUGIN
+        # if st.session_state['plugin'] == "🔌 PLUGIN NAME" and 'PLUGIN DB' in st.session_state:
+        #     with st.spinner('🚀 Using PLUGIN NAME...'):
+        #         solution = st.session_state['PLUGIN DB']({"query": prompt})
+        #         final_prompt = YourCustomPrompt(prompt, context)
+        if st.session_state['plugin'] == "📋 Talk with your DATA" and 'df' in st.session_state:
+            #get only last message
+            context = f"User: {st.session_state['past'][-1]}\nBot: {st.session_state['generated'][-1]}\n"
+            if prompt.find('python') != -1 or prompt.find('Code') != -1 or prompt.find('code') != -1 or prompt.find('Python') != -1:
+                with st.spinner('🚀 Using tool for python code...'):
+                    solution = "\n```python\n"
+                    solution += st.session_state['df'].sketch.howto(prompt, call_display=False)
+                    solution += "\n```\n\n"
+                    final_prompt = prompt4Code(prompt, context, solution)
+            else:
+                with st.spinner('🚀 Using tool to get information...'):
+                    solution = st.session_state['df'].sketch.ask(prompt, call_display=False)
+                    final_prompt = prompt4Data(prompt, context, solution)
+        elif st.session_state['plugin'] == "📝 Talk with your DOCUMENTS" and 'pdf' in st.session_state:
+            #get only last message
+            context = f"User: {st.session_state['past'][-1]}\nBot: {st.session_state['generated'][-1]}\n"
+            with st.spinner('🚀 Using tool to get information...'):
+                result = st.session_state['pdf']({"query": prompt})
+                solution = result["result"]
+                if len(solution.split()) > 110:
+                    make_better = False
+                    final_prompt = solution
+                    if 'source_documents' in result and len(result["source_documents"]) > 0:
+                        final_prompt += "\n\n✅Source:\n"
+                        for d in result["source_documents"]:
+                            final_prompt += "- " + str(d) + "\n"
+                else:
+                    final_prompt = prompt4Context(prompt, context, solution)
+                    if 'source_documents' in result and len(result["source_documents"]) > 0:
+                        source += "\n\n✅Source:\n"
+                        for d in result["source_documents"]:
+                            source += "- " + str(d) + "\n"
+        elif st.session_state['plugin'] == "🧠 GOD MODE" and 'god_mode' in st.session_state:
+            #get only last message
+            context = f"User: {st.session_state['past'][-1]}\nBot: {st.session_state['generated'][-1]}\n"
+            with st.spinner('🚀 Using tool to get information...'):
+                result = st.session_state['god_mode']({"query": prompt})
+                solution = result["result"]
+                if len(solution.split()) > 110:
+                    make_better = False
+                    final_prompt = solution
+                    if 'source_documents' in result and len(result["source_documents"]) > 0:
+                        final_prompt += "\n\n✅Source:\n"
+                        for d in result["source_documents"]:
+                            final_prompt += "- " + str(d) + "\n"
+                else:
+                    final_prompt = prompt4Context(prompt, context, solution)
+                    if 'source_documents' in result and len(result["source_documents"]) > 0:
+                        source += "\n\n✅Source:\n"
+                        for d in result["source_documents"]:
+                            source += "- " + str(d) + "\n"
+        elif st.session_state['plugin'] == "🔗 Talk with Website" and 'web_sites' in st.session_state:
+            #get only last message
+            context = f"User: {st.session_state['past'][-1]}\nBot: {st.session_state['generated'][-1]}\n"
+            with st.spinner('🚀 Using tool to get information...'):
+                result = st.session_state['web_sites']({"query": prompt})
+                solution = result["result"]
+                if len(solution.split()) > 110:
+                    make_better = False
+                    final_prompt = solution
+                    if 'source_documents' in result and len(result["source_documents"]) > 0:
+                        final_prompt += "\n\n✅Source:\n"
+                        for d in result["source_documents"]:
+                            final_prompt += "- " + str(d) + "\n"
+                else:
+                    final_prompt = prompt4Context(prompt, context, solution)
+                    if 'source_documents' in result and len(result["source_documents"]) > 0:
+                        source += "\n\n✅Source:\n"
+                        for d in result["source_documents"]:
+                            source += "- " + str(d) + "\n"
+        elif st.session_state['plugin'] == "💾 Upload saved VectorStore" and 'old_db' in st.session_state:
+            #get only last message
+            context = f"User: {st.session_state['past'][-1]}\nBot: {st.session_state['generated'][-1]}\n"
+            with st.spinner('🚀 Using tool to get information...'):
+                result = st.session_state['old_db']({"query": prompt})
+                solution = result["result"]
+                if len(solution.split()) > 110:
+                    make_better = False
+                    final_prompt = solution
+                    if 'source_documents' in result and len(result["source_documents"]) > 0:
+                        final_prompt += "\n\n✅Source:\n"
+                        for d in result["source_documents"]:
+                            final_prompt += "- " + str(d) + "\n"
+                else:
+                    final_prompt = prompt4Context(prompt, context, solution)
+                    if 'source_documents' in result and len(result["source_documents"]) > 0:
+                        source += "\n\n✅Source:\n"
+                        for d in result["source_documents"]:
+                            source += "- " + str(d) + "\n"
+        elif st.session_state['plugin'] == "🎧 Talk with your AUDIO" and 'audio' in st.session_state:
+            #get only last message
+            context = f"User: {st.session_state['past'][-1]}\nBot: {st.session_state['generated'][-1]}\n"
+            with st.spinner('🚀 Using tool to get information...'):
+                result = st.session_state['audio']({"query": prompt})
+                solution = result["result"]
+                if len(solution.split()) > 110:
+                    make_better = False
+                    final_prompt = solution
+                    if 'source_documents' in result and len(result["source_documents"]) > 0:
+                        final_prompt += "\n\n✅Source:\n"
+                        for d in result["source_documents"]:
+                            final_prompt += "- " + str(d) + "\n"
+                else:
+                    final_prompt = prompt4Audio(prompt, context, solution)
+                    if 'source_documents' in result and len(result["source_documents"]) > 0:
+                        source += "\n\n✅Source:\n"
+                        for d in result["source_documents"]:
+                            source += "- " + str(d) + "\n"
+        elif st.session_state['plugin'] == "🎥 Talk with YT video" and 'yt' in st.session_state:
+            context = f"User: {st.session_state['past'][-1]}\nBot: {st.session_state['generated'][-1]}\n"
+            with st.spinner('🚀 Using tool to get information...'):
+                result = st.session_state['yt']({"query": prompt})
+                solution = result["result"]
+                if len(solution.split()) > 110:
+                    make_better = False
+                    final_prompt = solution
+                    if 'source_documents' in result and len(result["source_documents"]) > 0:
+                        final_prompt += "\n\n✅Source:\n"
+                        for d in result["source_documents"]:
+                            final_prompt += "- " + str(d) + "\n"
+                else:
+                    final_prompt = prompt4YT(prompt, context, solution)
+                    if 'source_documents' in result and len(result["source_documents"]) > 0:
+                        source += "\n\n✅Source:\n"
+                        for d in result["source_documents"]:
+                            source += "- " + str(d) + "\n"
+        else:
+            #get last message if exists
+            if len(st.session_state['past']) == 1:
+                context = f"User: {st.session_state['past'][-1]}\nBot: {st.session_state['generated'][-1]}\n"
+            else:
+                context = f"User: {st.session_state['past'][-2]}\nBot: {st.session_state['generated'][-2]}\nUser: {st.session_state['past'][-1]}\nBot: {st.session_state['generated'][-1]}\n"
+            if 'web_search' in st.session_state:
+                if st.session_state['web_search'] == "True":
+                    with st.spinner('🚀 Using internet to get information...'):
+                        internet_result = ""
+                        internet_answer = ""
+                        with DDGS() as ddgs:
+                            ddgs_gen = ddgs.text(prompt, region=st.session_state['region'], safesearch=st.session_state['safesearch'], timelimit=st.session_state['timelimit'])
+                            for r in islice(ddgs_gen, st.session_state['max_results']):
+                                internet_result += str(r) + "\n\n"
+                            fast_answer = ddgs.answers(prompt)
+                            for r in islice(fast_answer, 2):
+                                internet_answer += str(r) + "\n\n"
+                        final_prompt = prompt4conversationInternet(prompt, context, internet_result, internet_answer)
+                else:
+                    final_prompt = prompt4conversation(prompt, context)
+            else:
+                final_prompt = prompt4conversation(prompt, context)
+        if make_better:
+            with st.spinner('🚀 Generating response...'):
+                print(final_prompt)
+                response = st.session_state['chatbot'].chat(final_prompt, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty, top_k=top_k, max_new_tokens=max_new_tokens)
+                response += source
+        else:
+            print(final_prompt)
+            response = final_prompt
+    return response
+## Conditional display of AI generated responses as a function of user provided prompts
+with response_container:
+    if input_text and 'hf_email' in st.session_state and 'hf_pass' in st.session_state:
+        response = generate_response(input_text)
+        st.session_state.past.append(input_text)
+        st.session_state.generated.append(response)
+    #print message in normal order, frist user then bot
+    if 'generated' in st.session_state:
+        if st.session_state['generated']:
+            for i in range(len(st.session_state['generated'])):
+                with st.chat_message(name="user"):
+                    st.markdown(st.session_state['past'][i])
+                with st.chat_message(name="assistant"):
+                    if len(st.session_state['generated'][i].split("✅Source:")) > 1:
+                        source = st.session_state['generated'][i].split("✅Source:")[1]
+                        mess = st.session_state['generated'][i].split("✅Source:")[0]
+                        st.markdown(mess)
+                        with st.expander("📚 Source of message number " + str(i+1)):
+                            st.markdown(source)
+                    else:
+                        st.markdown(st.session_state['generated'][i])
+            st.markdown('', unsafe_allow_html=True)
+    else:
+        st.info("👋 Hey , we are very happy to see you here 🤗")
+        st.info("👉 Please Login to continue, click on top left corner to login 🚀")
+        st.error("👉 If you are not registered on Hugging Face, please register first and then login 🤗")