from local

model/models--sentence-transformers--all-mpnet-base-v2/refs/main

@@ -0,0 +1 @@
+84f2bcc00d77236f9e89c8a360a00fb1139bf47d

model/models--sentence-transformers--all-mpnet-base-v2/snapshots/84f2bcc00d77236f9e89c8a360a00fb1139bf47d/1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

model/models--sentence-transformers--all-mpnet-base-v2/snapshots/84f2bcc00d77236f9e89c8a360a00fb1139bf47d/README.md

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+---
+language: en
+license: apache-2.0
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+datasets:
+- s2orc
+- flax-sentence-embeddings/stackexchange_xml
+- ms_marco
+- gooaq
+- yahoo_answers_topics
+- code_search_net
+- search_qa
+- eli5
+- snli
+- multi_nli
+- wikihow
+- natural_questions
+- trivia_qa
+- embedding-data/sentence-compression
+- embedding-data/flickr30k-captions
+- embedding-data/altlex
+- embedding-data/simple-wiki
+- embedding-data/QQP
+- embedding-data/SPECTER
+- embedding-data/PAQ_pairs
+- embedding-data/WikiAnswers
+pipeline_tag: sentence-similarity
+---
+
+
+# all-mpnet-base-v2
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+## Usage (Sentence-Transformers)
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Usage (HuggingFace Transformers)
+Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+import torch.nn.functional as F
+
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence', 'Each sentence is converted']
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-mpnet-base-v2')
+model = AutoModel.from_pretrained('sentence-transformers/all-mpnet-base-v2')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+# Perform pooling
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+# Normalize embeddings
+sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+## Evaluation Results
+
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-mpnet-base-v2)
+
+------
+
+## Background
+
+The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised 
+contrastive learning objective. We used the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model and fine-tuned in on a 
+1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
+
+We developped this model during the 
+[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), 
+organized by Hugging Face. We developped this model as part of the project:
+[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
+
+## Intended uses
+
+Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures 
+the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
+
+By default, input text longer than 384 word pieces is truncated.
+
+
+## Training procedure
+
+### Pre-training 
+
+We use the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model. Please refer to the model card for more detailed information about the pre-training procedure.
+
+### Fine-tuning 
+
+We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
+We then apply the cross entropy loss by comparing with true pairs.
+
+#### Hyper parameters
+
+We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
+We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
+a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.
+
+#### Training data
+
+We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
+We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
+
+
+| Dataset                                                  | Paper                                    | Number of training tuples  |
+|--------------------------------------------------------|:----------------------------------------:|:--------------------------:|
+| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
+| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
+| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
+| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
+| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
+| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs  | - | 25,316,456 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs  | - | 21,396,559 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs  | - | 21,396,559 |
+| [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
+| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
+| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 |
+| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
+| [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
+| [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
+| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
+| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 |
+| [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |
+| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
+| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 |
+| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 | 
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 |
+| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 |
+| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
+| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
+| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
+| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
+| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
+| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
+| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
+| [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
+| **Total** | | **1,170,060,424** |

model/models--sentence-transformers--all-mpnet-base-v2/snapshots/84f2bcc00d77236f9e89c8a360a00fb1139bf47d/config.json

@@ -0,0 +1,23 @@
+{
+  "_name_or_path": "microsoft/mpnet-base",
+  "architectures": [
+    "MPNetForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "transformers_version": "4.8.2",
+  "vocab_size": 30527
+}

model/models--sentence-transformers--all-mpnet-base-v2/snapshots/84f2bcc00d77236f9e89c8a360a00fb1139bf47d/config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.6.1",
+    "pytorch": "1.8.1"
+  }
+}

model/models--sentence-transformers--all-mpnet-base-v2/snapshots/84f2bcc00d77236f9e89c8a360a00fb1139bf47d/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:78c0197b6159d92658e319bc1d72e4c73a9a03dd03815e70e555c5ef05615658
+size 437971872

model/models--sentence-transformers--all-mpnet-base-v2/snapshots/84f2bcc00d77236f9e89c8a360a00fb1139bf47d/modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

model/models--sentence-transformers--all-mpnet-base-v2/snapshots/84f2bcc00d77236f9e89c8a360a00fb1139bf47d/sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 384,
+  "do_lower_case": false
+}

model/models--sentence-transformers--all-mpnet-base-v2/snapshots/84f2bcc00d77236f9e89c8a360a00fb1139bf47d/special_tokens_map.json

@@ -0,0 +1 @@
+{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "[UNK]", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": false}}

model/models--sentence-transformers--all-mpnet-base-v2/snapshots/84f2bcc00d77236f9e89c8a360a00fb1139bf47d/tokenizer_config.json

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+{"do_lower_case": true, "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "[UNK]", "pad_token": "<pad>", "mask_token": "<mask>", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "microsoft/mpnet-base", "tokenizer_class": "MPNetTokenizer"}

model_download.py

@@ -0,0 +1,17 @@
+# from transformers import AutoModel, AutoTokenizer
+# from langchain.embeddings import HuggingFaceEmbeddings
+
+# local_model_path = "models"
+
+# # Download the model and tokenizer
+# model_name = "sentence-transformers/all-mpnet-base-v2"
+# AutoModel.from_pretrained(model_name, cache_dir=local_model_path)
+# AutoTokenizer.from_pretrained(model_name, cache_dir=local_model_path)
+
+from sentence_transformers import SentenceTransformer
+
+local_model_path = "model"
+
+# Download the model
+model_name = "sentence-transformers/all-mpnet-base-v2"
+model = SentenceTransformer(model_name, cache_folder=local_model_path)

source.txt

@@ -0,0 +1 @@
+https://github.com/IntelligenzaArtificiale/Free-personal-AI-Assistant-with-plugin/

streamlit_app.py

@@ -0,0 +1,1012 @@
+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_community.vectorstores import Chroma
+from langchain_community.embeddings import HuggingFaceHubEmbeddings
+
+from langchain.chains import RetrievalQA
+from HuggingChatAPI import HuggingChat
+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
+from langchain_community.embeddings import HuggingFaceEmbeddings
+
+hf = None
+# repo_id = "sentence-transformers/all-mpnet-base-v2"
+# repo_id = "nomic-ai/nomic-embed-text-v1.5"
+
+
+# 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
+
+hf = None
+local_model_path = "model"
+model_name = "sentence-transformers/all-mpnet-base-v2"
+hf = None
+if 'hf' not in st.session_state:
+    hf = HuggingFaceEmbeddings(
+        model_name=model_name,
+        cache_folder=local_model_path,
+    )
+    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())
+                        a_models = chatbot.get_available_llm_models()
+                        # print(a_models)
+                    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'] = ["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'], model=0)
+                    
+                    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=500, max_value=4000, value=4000, step=50)
+    
+
+        # 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=150)
+                            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=150)
+                        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=150)
+                        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=1500, chunk_overlap=150)
+                            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(search_type="mmr", search_kwargs={'k': 100, 'lambda_mult': 0.25})
+                            # 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=150)
+                            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 🤗")