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import gradio as gr
from llama_index.core import VectorStoreIndex,StorageContext
from llama_index.core.memory import ChatMemoryBuffer
import re
from llama_index.core import get_response_synthesizer
from llama_index.core.query_engine import RetrieverQueryEngine
# Retrievers
from llama_index.core.retrievers import (
VectorIndexRetriever,
)
from llama_index.core.chat_engine import ContextChatEngine
from llama_index.core.memory import ChatMemoryBuffer
from pinecone import Pinecone
from llama_index.vector_stores.pinecone import PineconeVectorStore
import time
from utils import *
import spaces
import threading
import sys
import torch
from llama_index.core.vector_stores import (
MetadataFilter,
MetadataFilters,
FilterOperator,
FilterCondition,
)
head = """
<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.3/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-QWTKZyjpPEjISv5WaRU9OFeRpok6YctnYmDr5pNlyT2bRjXh0JMhjY6hW+ALEwIH" crossorigin="anonymous">
<script>
// JavaScript function to toggle text visibility
function toggleText(id){
console.log(id)
if(id=="span1"){
nodo_id = "nodo1"
}else if(id=="span2"){
nodo_id = "nodo2"
}else if(id=="span3"){
nodo_id = "nodo3"
}else{
nodo_id = "nodo4"
}
var text = document.getElementById(nodo_id);
if (text.style.display === "none") {
text.style.display = "block";
} else {
text.style.display = "none";
}
}
</script>
"""
css = """
#chatbot {
margin-top: 1%;
width: 75%;
position:relative;
height:70%;
}
#textBox{
width: 75%;
position:relative;
}
.wrapper.svelte-nab2ao p{
font-size: 14px;
}
#btnClear{
width: 75%;
}
#buttonChat{
width:50%;
position: relative;
}
#colonnaElementi{
position: absolute;
left: 77%;
top: 10%;
bottom: 10%; /* Adjust this value as necessary */
width: 10%;
height: auto; /* Let the height be determined by the top and bottom properties */
max-height: 80%; /* Ensure it does not exceed 80% of the parent container's height */
overflow-y: auto; /* Allow scrolling if content overflows vertically */
overflow-x: hidden; /* Hide horizontal overflow */
word-wrap: break-word; /* Ensure words break to fit within the width */
box-sizing: border-box; /* Include padding and border in the element's total width and height */
}
#responseMode{
width: 5%;
}
.message.user.svelte-gutj6d.message-bubble-border{
padding: 5px;
}
.message.bot.svelte-gutj6d.message-bubble-border{
padding: 5px;
}
.icon {
cursor: pointer;
}
/* Style for the hidden text */
.hidden-text {
display: none;
}
.wrap svelte-1sk0pyu{
width: 12%
}
"""
user_message=""
current_chat_mode=""
current_response_mode="tree_summarize"
current_collection="BANDI_SISTEMA_PUGLIA"
file_path=""
num_responses=0
current_chat_mode="STANDARD"
retriever=None
index = None
token_count_bandi=0
token_count_bandi_sistema_puglia=0
chat_engine_bandi=None
chat_engine_bandi_sistema_puglia=None
memory_bandi=None
memory_bandi_sistema_puglia=None
stream_response=None
divDocumenti=None
llm = None
def main():
global llm
setGPU()
llm = setLLM()
Settings.llm = llm
Settings.embed_model = "local:google-bert/bert-base-multilingual-cased"
embed_model = Settings.embed_model
text_qa_template, refine_template = setPromptTemplate()
def select_initial_collection():
global current_collection
global retriever
global index
pc = Pinecone(api_key="7e412663-a2dc-44a6-ab57-25dd0bdce226")
# connect to index
pinecone_index = pc.Index("indexbandisistemapuglia")
vector_store = PineconeVectorStore(
pinecone_index=pinecone_index,
add_sparse_vector=True,
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_vector_store(
vector_store, storage_context=storage_context
)
retriever = VectorIndexRetriever(index=index, similarity_top_k=3, vector_store_query_mode="hybrid", embed_model=embed_model, alpha=0.5)
current_collection = "BANDI_SISTEMA_PUGLIA"
return "collezione settata"
select_initial_collection()
def select_collection(evt: gr.SelectData):
global current_collection
global retriever
global index
global chat_engine_bandi
global chat_engine_bandi_sistema_puglia
global token_count_bandi
global token_count_bandi_sistema_puglia
global memory_bandi
global memory_bandi_sistema_puglia
selected_collection = evt.value
if(selected_collection != current_collection):
if(selected_collection == "BANDI_SISTEMA_PUGLIA"):
chat_engine_bandi.reset()
chat_engine_bandi_sistema_puglia.reset()
memory_bandi_sistema_puglia.reset()
memory_bandi.reset()
token_count_bandi = 0
token_count_bandi_sistema_puglia = 0
pc = Pinecone(api_key="7e412663-a2dc-44a6-ab57-25dd0bdce226")
# connect to index
pinecone_index = pc.Index("indexbandisistemapuglia")
vector_store = PineconeVectorStore(
pinecone_index=pinecone_index,
add_sparse_vector=True,
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
# load your index from stored vectors
index = VectorStoreIndex.from_vector_store(
vector_store, storage_context=storage_context
)
retriever = VectorIndexRetriever(index=index, similarity_top_k=3, vector_store_query_mode="hybrid", embed_model=embed_model, alpha=0.5)
else:
chat_engine_bandi.reset()
chat_engine_bandi_sistema_puglia.reset()
memory_bandi_sistema_puglia.reset()
memory_bandi.reset()
token_count_bandi = 0
token_count_bandi_sistema_puglia = 0
pc = Pinecone(api_key="7e412663-a2dc-44a6-ab57-25dd0bdce226")
# connect to index
pinecone_index = pc.Index("indexbandi")
vector_store = PineconeVectorStore(
pinecone_index=pinecone_index,
add_sparse_vector=True,
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
# load your index from stored vectors
index = VectorStoreIndex.from_vector_store(
vector_store, storage_context=storage_context
)
retriever = VectorIndexRetriever(index=index, similarity_top_k=3, vector_store_query_mode="hybrid", embed_model=embed_model, alpha=0.4)
current_collection = selected_collection
return "<div class='alert alert-success' role='alert'> Collezione "+selected_collection+" selezionata </div>"
def select_response_mode(evt: gr.SelectData):
global current_response_mode
current_response_mode = evt.value
return "<div class='alert alert-success' role='alert'>"+current_response_mode+" selezionato </div>"
def select_chat_mode():
global current_chat_mode
global memory_bandi
global memory_bandi_sistema_puglia
global chat_engine_bandi
global chat_engine_bandi_sistema_puglia
global token_count_bandi
global token_count_bandi_sistema_puglia
memory_bandi_sistema_puglia.reset()
memory_bandi.reset()
chat_engine_bandi.reset()
chat_engine_bandi_sistema_puglia.reset()
token_count_bandi = 0
token_count_bandi_sistema_puglia = 0
current_chat_mode = "CHAT"
return "<div class='alert alert-success' role='alert'>Hai selezionato la modalità "+current_chat_mode+" </div>"
def select_standard_mode():
global current_chat_mode
current_chat_mode = "STANDARD"
return "<div class='alert alert-success' role='alert'>Hai selezionato la modalità "+current_chat_mode+" </div>"
def set_chat_engine():
global chat_engine_bandi
global chat_engine_bandi_sistema_puglia
global memory_bandi
global memory_bandi_sistema_puglia
global token_count_bandi_sistema_puglia
global token_count_bandi
memory_bandi = ChatMemoryBuffer.from_defaults(token_limit=5000)
memory_bandi_sistema_puglia = ChatMemoryBuffer.from_defaults(token_limit=3000)
pc = Pinecone(api_key="7e412663-a2dc-44a6-ab57-25dd0bdce226")
pinecone_index = pc.Index("indexbandi")
vector_store = PineconeVectorStore(
pinecone_index=pinecone_index,
add_sparse_vector=True,
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_vector_store(
vector_store, storage_context=storage_context
)
retriever_bandi = VectorIndexRetriever(index=index, similarity_top_k=3, vector_store_query_mode="hybrid", embed_model=embed_model, alpha=0.4)
chat_engine_bandi = ContextChatEngine(retriever=retriever_bandi,
context_template="Sei un chatbot in grado di rispondere alle domande su bandi regionali e avvisi della regione Puglia. Hai accesso ai bandi della regione Puglia. Qui sotto le informazioni di contesto recuperate. \n"
"---------------------\n"
"Informazioni di contesto: "+"{context_str}\n"
"---------------------\n"
"Usa le informazioni di contesto sopra fornite e non la tua conoscenza pregressa per rispondere, l'unica regione che conosci è la regione Puglia. "
"rispondi sempre alla seguente query sul bando regionale della Puglia usando le informazioni di contesto."
"\n", llm=llm, memory=memory_bandi, prefix_messages=[])
pinecone_index = pc.Index("indexbandisistemapuglia")
vector_store = PineconeVectorStore(
pinecone_index=pinecone_index,
add_sparse_vector=True,
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_vector_store(
vector_store, storage_context=storage_context
)
retriever_bandi_sistema_puglia = VectorIndexRetriever(index=index, similarity_top_k=3, vector_store_query_mode="hybrid", embed_model=embed_model, alpha=0.5)
chat_engine_bandi_sistema_puglia = ContextChatEngine(retriever=retriever_bandi_sistema_puglia,
context_template="Sei un chatbot in grado di rispondere alle domande su bandi regionali e avvisi della regione Puglia. Hai accesso ai bandi della regione Puglia. Qui sotto le informazioni di contesto recuperate. \n"
"---------------------\n"
"Informazioni di contesto: "+"{context_str}\n"
"---------------------\n"
"Usa le informazioni di contesto sopra fornite e non la tua conoscenza pregressa per rispondere, l'unica regione che conosci è la regione Puglia. "
"rispondi sempre alla seguente query sul bando regionale della Puglia usando le informazioni di contesto."
"\n", llm=llm, memory=memory_bandi_sistema_puglia, prefix_messages=[])
def html_escape(text):
html_entities = {
'à': 'à',
'è': 'è',
'é': 'é',
'ì': 'ì',
'ò': 'ò',
'ù': 'ù',
'À': 'À',
'È': 'È',
'É': 'É',
'Ì': 'Ì',
'Ò': 'Ò',
'Ù': 'Ù',
'ç': 'ç',
'Ç': 'Ç',
'ä': 'ä',
'ö': 'ö',
'ü': 'ü',
'Ä': 'Ä',
'Ö': 'Ö',
'Ü': 'Ü',
'ß': 'ß',
'ñ': 'ñ',
'Ñ': 'Ñ',
'œ': 'œ',
'Œ': 'Œ',
'æ': 'æ',
'Æ': 'Æ',
'ø': 'ø',
'Ø': 'Ø',
'å': 'å',
'Å': 'Å',
'&': '&',
'<': '<',
'>': '>',
'"': '"',
"'": '''
}
return ''.join(html_entities.get(c, c) for c in text)
def reset():
global chat_engine_bandi
global chat_engine_bandi_sistema_puglia
global memory_bandi
global memory_bandi_sistema_puglia
global token_count_bandi
global token_count_bandi_sistema_puglia
chat_engine_bandi.reset()
chat_engine_bandi_sistema_puglia.reset()
memory_bandi_sistema_puglia.reset()
memory_bandi.reset()
token_count_bandi = 0
token_count_bandi_sistema_puglia = 0
return "<div class='alert alert-success' role='alert'>Cronologia chat eliminata</div>"
set_chat_engine()
with gr.Blocks(css=css, head=head) as demo:
with gr.Row():
output = gr.HTML()
with gr.Row(elem_id="buttonChat"):
gr.Button("STANDARD", size="sm").click(fn=select_standard_mode, outputs=output)
gr.Button("CHAT",size="sm").click(fn=select_chat_mode, outputs=output)
gr.Dropdown(
["BANDI_SISTEMA_PUGLIA","BANDI"], min_width= 185, label="Collezione di documenti", info="", container=False, interactive=True, value="BANDI_SISTEMA_PUGLIA", elem_id="dropdown"
).select(fn=select_collection, outputs=output)
chatbot = gr.Chatbot(elem_id="chatbot", container=False)
with gr.Column(elem_id="colonnaElementi"):
gr.Radio(["compact","tree_summarize"], label="Response mode", info="Influenzerà il modo in cui il chatbot risponde", interactive=True,container=False, value="tree_summarize",elem_id="responseMode").select(fn=select_response_mode, outputs=output),
divDocumenti = gr.HTML("<div id='divDocumenti'></div>")
msg = gr.Textbox(elem_id="textBox", container=False)
clear = gr.ClearButton([msg, chatbot], elem_id="btnClear")
clear.click(fn=reset, outputs=output)
def user(userMessage, history):
global user_message
user_message = userMessage
if history is None:
history = []
return "", history + [[user_message, None]]
@spaces.GPU(duration=150)
def bot(history):
lenght = len(history)
userMessage = history[lenght-1][0]
global chat_engine_bandi
global chat_engine_bandi_sistema_puglia
global memory_bandi
global memory_bandi_sistema_puglia
global current_response_mode
global current_collection
global retriever
global file_path
global current_chat_mode
global token_count_bandi
global token_count_bandi_sistema_puglia
if(current_chat_mode=="CHAT"):
print("MODALITA CHAT")
if(current_collection=="BANDI"):
if(token_count_bandi >= 1000):
print("RESET!!!")
token_count_bandi = 0
memory_bandi.reset()
chat_engine_bandi.reset()
print(chat_engine_bandi.chat_history)
print(memory_bandi)
stream_response = None
print(userMessage)
stream_response = chat_engine_bandi.stream_chat(userMessage)
print("risposta con chat engine")
responseHTML = ""
for i, node in enumerate(stream_response.source_nodes):
responseHTML += "<p><b>"+node.metadata['nome_bando']+"</b><a href='"+node.metadata['file_path']+"' download> <svg xmlns='http://www.w3.org/2000/svg' width='16' height='16' fill='currentColor' class='bi bi-download' viewBox='0 0 16 16'><path d='M.5 9.9a.5.5 0 0 1 .5.5v2.5a1 1 0 0 0 1 1h12a1 1 0 0 0 1-1v-2.5a.5.5 0 0 1 1 0v2.5a2 2 0 0 1-2 2H2a2 2 0 0 1-2-2v-2.5a.5.5 0 0 1 .5-.5'/><path d='M7.646 11.854a.5.5 0 0 0 .708 0l3-3a.5.5 0 0 0-.708-.708L8.5 10.293V1.5a.5.5 0 0 0-1 0v8.793L5.354 8.146a.5.5 0 1 0-.708.708z'/> </svg></a><br>Nodo <span id='span"+str(i+1)+"' class='icon' onclick='toggleText(this.id)'>🔍</span> <!-- Text to show/hide --><p class='hidden-text' id='nodo"+str(i+1)+"'>"+node.text+"</p>"
history[-1][1] = ""
for character in stream_response.response_gen:
tokens = character.split(" ")
num_tokens = len(tokens)
token_count_bandi = token_count_bandi + num_tokens
print(token_count_bandi)
history[-1][1] += html_escape(str(character))
time.sleep(0.05)
yield history, responseHTML
else:
if(token_count_bandi_sistema_puglia >= 1000):
print("RESET!!!")
token_count_bandi_sistema_puglia = 0
memory_bandi_sistema_puglia.reset()
chat_engine_bandi_sistema_puglia.reset()
print(chat_engine_bandi_sistema_puglia.chat_history)
print(memory_bandi_sistema_puglia)
stream_response = None
print(userMessage)
stream_response = chat_engine_bandi_sistema_puglia.stream_chat(userMessage)
print("risposta con chat engine")
responseHTML = ""
for i, node in enumerate(stream_response.source_nodes):
responseHTML += "<p><b>"+node.metadata['nome_bando']+"</b><a href='"+node.metadata['file_path']+"' download> <svg xmlns='http://www.w3.org/2000/svg' width='16' height='16' fill='currentColor' class='bi bi-download' viewBox='0 0 16 16'><path d='M.5 9.9a.5.5 0 0 1 .5.5v2.5a1 1 0 0 0 1 1h12a1 1 0 0 0 1-1v-2.5a.5.5 0 0 1 1 0v2.5a2 2 0 0 1-2 2H2a2 2 0 0 1-2-2v-2.5a.5.5 0 0 1 .5-.5'/><path d='M7.646 11.854a.5.5 0 0 0 .708 0l3-3a.5.5 0 0 0-.708-.708L8.5 10.293V1.5a.5.5 0 0 0-1 0v8.793L5.354 8.146a.5.5 0 1 0-.708.708z'/> </svg></a><br>Nodo <span id='span"+str(i+1)+"' class='icon' onclick='toggleText(this.id)'>🔍</span> <!-- Text to show/hide --><p class='hidden-text' id='nodo"+str(i+1)+"'>"+node.text+"</p>"
history[-1][1] = ""
for character in stream_response.response_gen:
tokens = character.split(" ")
num_tokens = len(tokens)
token_count_bandi_sistema_puglia = token_count_bandi_sistema_puglia + num_tokens
print(token_count_bandi_sistema_puglia)
history[-1][1] += html_escape(str(character))
time.sleep(0.05)
yield history,responseHTML
else:
print("MODALITA STANDARD")
if(str(current_response_mode)=="tree_summarize"):
nome_bando = ""
userMessage = userMessage.lower()
if("diploma professionale" in userMessage):
nome_bando += "Scheda Avviso Pubblico Diploma Professionale 2022.pdf,"
if("red" in userMessage):
nome_bando += "Scheda RED 2020.pdf,"
if("ifts" in userMessage):
nome_bando += "Scheda Avviso Pubblico IFTS_2023.pdf,"
if(("impianti" in userMessage) or ("idrogeno" in userMessage)):
nome_bando += "Scheda Avviso PNRR - Impianti idrogeno rinnovabile.pdf,"
if("laureati" in userMessage):
nome_bando += "Scheda Pass Laureati 2023.pdf,"
if("nidi" in userMessage):
nome_bando += "Scheda NIDI - Nuove iniziative d'impresa_ Strumento di ingegneria finanziaria.pdf,"
if("microprestito" in userMessage):
nome_bando += "Scheda MicroPrestito della Regione Puglia - edizione 2021.pdf,"
if("gol" in userMessage):
nome_bando += "Scheda Garanzia di occupabilità dei lavoratori - GOL.pdf,"
if("edifici pubblici" in userMessage):
nome_bando += "Scheda Efficientamento Energetico Edifici Pubblici.pdf,"
if("innoaid" in userMessage):
nome_bando += "Scheda di sintesi Avviso _INNOAID - RIAPERTURA_.pdf,"
if("tecnonidi" in userMessage):
nome_bando += "Scheda Avviso Tecnonidi - Aiuti alle piccole imprese innovative.pdf,"
if("of" in userMessage):
nome_bando += "Scheda Avviso Pubblico OF a_f_ 2023_2024.pdf,"
if("giardiniere" in userMessage):
nome_bando += "Scheda Avviso Pubblico _Giardiniere d'arte per giardini e parchi storici_.pdf,"
if("punti cardinali" in userMessage):
nome_bando += "Scheda Avviso _Punti Cardinali_ punti di orientamento per la formazione e il lavoro.pdf,"
if("accreditamento servizi" in userMessage):
nome_bando += "Scheda Accreditamento Servizi per il Lavoro.pdf,"
if("accreditamento organismi" in userMessage):
nome_bando += "Scheda Accreditamento Organismi Formativi.pdf,"
if("multimisura POC" in userMessage):
nome_bando += "Avviso Multimisura POC.pdf,"
if("garanzia giovani" in userMessage):
nome_bando += "Avviso Multimisura - Garanzia Giovani II Fase.pdf,"
if("apprendistato professionalizzante" in userMessage):
nome_bando += "Apprendistato Professionalizzante.pdf,"
# Rimuovi l'ultima virgola
if nome_bando.endswith(","):
nome_bando = nome_bando[:-1]
# Crea una lista di bandi separati dalla virgola
lista_bandi = nome_bando.split(",")
# Crea una lista di oggetti MetadataFilter
filter_list = []
for bando in lista_bandi:
filter_list.append(MetadataFilter(key="nome_bando", value=bando))
#crea una lista di MetadataFilter
filters = MetadataFilters(
filters=filter_list,
condition=FilterCondition.OR,
)
retriever = VectorIndexRetriever(index=index, similarity_top_k=3, vector_store_query_mode="hybrid", embed_model=embed_model, alpha=0.5, filters=filters)
# define response synthesizer
response_synthesizer = get_response_synthesizer(streaming=True,response_mode="tree_summarize",text_qa_template=text_qa_template)
query_engine = None
query_engine = RetrieverQueryEngine(retriever=retriever, response_synthesizer=response_synthesizer)
stream_response = None
print(userMessage)
stream_response = query_engine.query(userMessage)
print("risposta con query engine")
responseHTML = ""
for i, node in enumerate(stream_response.source_nodes):
responseHTML += "<p><b>"+node.metadata['nome_bando']+"</b><a href='"+node.metadata['file_path']+"' download> <svg xmlns='http://www.w3.org/2000/svg' width='16' height='16' fill='currentColor' class='bi bi-download' viewBox='0 0 16 16'><path d='M.5 9.9a.5.5 0 0 1 .5.5v2.5a1 1 0 0 0 1 1h12a1 1 0 0 0 1-1v-2.5a.5.5 0 0 1 1 0v2.5a2 2 0 0 1-2 2H2a2 2 0 0 1-2-2v-2.5a.5.5 0 0 1 .5-.5'/><path d='M7.646 11.854a.5.5 0 0 0 .708 0l3-3a.5.5 0 0 0-.708-.708L8.5 10.293V1.5a.5.5 0 0 0-1 0v8.793L5.354 8.146a.5.5 0 1 0-.708.708z'/> </svg></a><br>Nodo <span id='span"+str(i+1)+"' class='icon' onclick='toggleText(this.id)'>🔍</span> <!-- Text to show/hide --><p class='hidden-text' id='nodo"+str(i+1)+"'>"+node.text+"</p>"
history[-1][1] = ""
for character in stream_response.response_gen:
history[-1][1] += html_escape(str(character))
time.sleep(0.05)
yield history, responseHTML
else:
# define response synthesizer
response_synthesizer = get_response_synthesizer(streaming=True,response_mode="compact",text_qa_template=text_qa_template, refine_template=refine_template)
query_engine = None
query_engine = RetrieverQueryEngine(retriever=retriever, response_synthesizer=response_synthesizer)
stream_response = None
print(userMessage)
stream_response = query_engine.query(userMessage)
print("risposta con query engine")
responseHTML = ""
for i, node in enumerate(stream_response.source_nodes):
responseHTML += "<p><b>"+node.metadata['nome_bando']+"</b><a href='"+node.metadata['file_path']+"' download> <svg xmlns='http://www.w3.org/2000/svg' width='16' height='16' fill='currentColor' class='bi bi-download' viewBox='0 0 16 16'><path d='M.5 9.9a.5.5 0 0 1 .5.5v2.5a1 1 0 0 0 1 1h12a1 1 0 0 0 1-1v-2.5a.5.5 0 0 1 1 0v2.5a2 2 0 0 1-2 2H2a2 2 0 0 1-2-2v-2.5a.5.5 0 0 1 .5-.5'/><path d='M7.646 11.854a.5.5 0 0 0 .708 0l3-3a.5.5 0 0 0-.708-.708L8.5 10.293V1.5a.5.5 0 0 0-1 0v8.793L5.354 8.146a.5.5 0 1 0-.708.708z'/> </svg></a><br>Nodo <span id='span"+str(i+1)+"' class='icon' onclick='toggleText(this.id)'>🔍</span> <!-- Text to show/hide --><p class='hidden-text' id='nodo"+str(i+1)+"'>"+node.text+"</p>"
history[-1][1] = ""
for character in stream_response.response_gen:
history[-1][1] += html_escape(str(character))
time.sleep(0.05)
yield history, responseHTML
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_max_memory_cached()
msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
bot, chatbot, [chatbot, divDocumenti]
)
demo.queue()
demo.launch(debug=True, share=True)
if __name__ == "__main__":
main() |