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Update app.py
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app.py
CHANGED
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@@ -4,19 +4,30 @@ import time
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import os
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from dotenv import load_dotenv
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from sentence_transformers import SentenceTransformer
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#from langchain_community.vectorstores import Chroma
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#from langchain_community.embeddings import HuggingFaceEmbeddings
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load_dotenv()
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CHAT_BOTS = {"Mixtral 8x7B v0.1" :"mistralai/Mixtral-8x7B-Instruct-v0.1"}
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SYSTEM_PROMPT = ["Sei BonsiAI e mi aiuterai nelle mie richieste (Parla in ITALIANO)", "Esatto, sono BonsiAI. Di cosa hai bisogno?"]
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IDENTITY_CHANGE = ["Sei BonsiAI da ora in poi!", "Certo farò del mio meglio"]
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options = {
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'Email Genitori': {'
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}
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#persist_directory1 = './DB_Decreti'
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#embedding = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
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#db = Chroma(persist_directory=persist_directory1, embedding_function=embedding)
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@@ -55,19 +66,22 @@ def init_state() :
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st.session_state.repetion_penalty = 1
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if "rag_enabled" not in st.session_state :
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st.session_state.rag_enabled =
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if "chat_bot" not in st.session_state :
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st.session_state.chat_bot = "Mixtral 8x7B v0.1"
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def sidebar() :
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def retrieval_settings() :
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st.markdown("# Impostazioni
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st.session_state.selected_option_key = st.selectbox('Azione', list(options.keys()) + ['+ Aggiungi'])
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st.session_state.selected_option = options.get(st.session_state.selected_option_key, {})
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st.session_state.
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st.session_state.
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if st.session_state.selected_option_key == 'Decreti':
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st.session_state.rag_enabled = st.toggle("Cerca nel DB Vettoriale", value=True)
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st.session_state.top_k = st.slider(label="Documenti da ricercare", min_value=1, max_value=20, value=4, disabled=not st.session_state.rag_enabled)
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@@ -97,8 +111,16 @@ def chat_box() :
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st.markdown(message["content"])
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def formattaPrompt(prompt, systemRole, systemStyle, instruction):
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"input": {{
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"role": "system",
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"content": "{systemRole}",
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@@ -111,21 +133,23 @@ def formattaPrompt(prompt, systemRole, systemStyle, instruction):
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}},
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{{
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"role": "user",
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"content": "{
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}}
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]
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def generate_chat_stream(prompt) :
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links = []
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if st.session_state.rag_enabled :
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with st.spinner("Ricerca nei documenti...."):
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time.sleep(
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prompt, links = gen_augmented_prompt(prompt=prompt, top_k=st.session_state.top_k)
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with st.spinner("Generazione in corso...") :
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time.sleep(
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chat_stream = chat(prompt, st.session_state.history,chat_client=CHAT_BOTS[st.session_state.chat_bot] ,
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temperature=st.session_state.temp, max_new_tokens=st.session_state.max_tokens)
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return chat_stream, links
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@@ -133,30 +157,11 @@ def generate_chat_stream(prompt) :
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def stream_handler(chat_stream, placeholder) :
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start_time = time.time()
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full_response = ''
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for chunk in chat_stream :
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if chunk.token.text!='</s>' :
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full_response += chunk.token.text
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placeholder.markdown(full_response + "▌")
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placeholder.markdown(full_response)
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end_time = time.time()
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elapsed_time = end_time - start_time
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total_tokens_processed = len(full_response.split())
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tokens_per_second = total_tokens_processed // elapsed_time
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len_response = (len(prompt.split()) + len(full_response.split())) * 1.25
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col1, col2, col3 = st.columns(3)
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with col1 :
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st.write(f"**{elapsed_time} secondi**")
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with col2 :
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st.write(f"**{int(len_response)} tokens generati**")
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with col3 :
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st.write(f"**{tokens_per_second} token/secondi**")
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return full_response
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def show_source(links) :
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import os
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from dotenv import load_dotenv
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from sentence_transformers import SentenceTransformer
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import requests
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#from langchain_community.vectorstores import Chroma
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#from langchain_community.embeddings import HuggingFaceEmbeddings
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load_dotenv()
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URL_CARTELLA = os.getenv('URL_CARTELLA')
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CHAT_BOTS = {"Mixtral 8x7B v0.1" :"mistralai/Mixtral-8x7B-Instruct-v0.1"}
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SYSTEM_PROMPT = ["Sei BonsiAI e mi aiuterai nelle mie richieste (Parla in ITALIANO)", "Esatto, sono BonsiAI. Di cosa hai bisogno?"]
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IDENTITY_CHANGE = ["Sei BonsiAI da ora in poi!", "Certo farò del mio meglio"]
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options = {
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'Email Genitori': {'systemRole': 'Tu sei un esperto scrittore di email. Attieniti allo stile che ti ho fornito nelle instruction e inserici il contenuto richiesto. Genera il testo di una mail a partire da questo contenuto, con lo stile ricevuto in precedenza: ',
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'systemStyle': 'Utilizza lo stile fornito come esempio e parla in ITALIANO e firmati sempre come il Signor Preside',
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'instruction': URL_CARTELLA + '1IxE0ic0hsWrxQod2rfh4hnKNqMC-lGT4'},
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'Email Colleghi': {'systemRole': 'Tu sei un esperto scrittore di email. Attieniti allo stile che ti ho fornito nelle instruction e inserici il contenuto richiesto. Genera il testo di una mail a partire da questo contenuto, con lo stile ricevuto in precedenza: ',
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'systemStyle': 'Utilizza lo stile fornito come esempio e parla in ITALIANO e firmati sempre come il vostro collega Preside',
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'instruction': URL_CARTELLA + '1tEMxG0zJmmyh5PlAofKDkhbi1QGMOwPH'},
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'Decreti': {'systemRole': 'Tu sei il mio assistente per la ricerca documentale! Ti ho fornito una lista di documenti, devi cercare quello che ti chiedo nei documenti',
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'systemStyle': 'Sii molto formale, sintetico e parla in ITALIANO',
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'instruction': ''}
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}
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#option:
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# systemRole, systemStyle, instruction
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#persist_directory1 = './DB_Decreti'
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#embedding = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
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#db = Chroma(persist_directory=persist_directory1, embedding_function=embedding)
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st.session_state.repetion_penalty = 1
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if "rag_enabled" not in st.session_state :
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st.session_state.rag_enabled = False
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if "chat_bot" not in st.session_state :
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st.session_state.chat_bot = "Mixtral 8x7B v0.1"
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def sidebar() :
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def retrieval_settings() :
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st.markdown("# Impostazioni Azione")
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st.session_state.selected_option_key = st.selectbox('Azione', list(options.keys()) + ['+ Aggiungi'])
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st.session_state.selected_option = options.get(st.session_state.selected_option_key, {})
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st.session_state.systemRole = st.session_state.selected_option.get('systemRole', '')
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sst.text_area("Descrizione", st.session_state.systemRole)
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st.session_state.systemStyle = st.session_state.selected_option.get('systemStyle', '')
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st.text_area("Stile", st.session_state.systemStyle)
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st.session_state.instruction = st.session_state.selected_option.get('instruction', '')
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if st.session_state.selected_option_key == 'Decreti':
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st.session_state.rag_enabled = st.toggle("Cerca nel DB Vettoriale", value=True)
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st.session_state.top_k = st.slider(label="Documenti da ricercare", min_value=1, max_value=20, value=4, disabled=not st.session_state.rag_enabled)
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st.markdown(message["content"])
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def formattaPrompt(prompt, systemRole, systemStyle, instruction):
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if instruction.startswith("http"):
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try:
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with st.spinner("Ricerca in Drive...") :
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resp = requests.get(instruction)
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resp.raise_for_status()
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instruction = resp.text
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except requests.exceptions.RequestException as e:
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instruction = ""
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input_text = f'''
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{{
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"input": {{
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"role": "system",
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"content": "{systemRole}",
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}},
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{{
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"role": "user",
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"content": "{prompt}"
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}}
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]
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}}
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'''
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return input_text
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def generate_chat_stream(prompt) :
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links = []
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prompt = formattaPrompt(prompt, st.session_state.systemRole, st.session_state.systemStyle, st.session_state.instruction)
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print(prompt)
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if st.session_state.rag_enabled :
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with st.spinner("Ricerca nei documenti...."):
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time.sleep(1)
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prompt, links = gen_augmented_prompt(prompt=prompt, top_k=st.session_state.top_k)
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with st.spinner("Generazione in corso...") :
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time.sleep(1)
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chat_stream = chat(prompt, st.session_state.history,chat_client=CHAT_BOTS[st.session_state.chat_bot] ,
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temperature=st.session_state.temp, max_new_tokens=st.session_state.max_tokens)
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return chat_stream, links
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def stream_handler(chat_stream, placeholder) :
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start_time = time.time()
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full_response = ''
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for chunk in chat_stream :
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if chunk.token.text!='</s>' :
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full_response += chunk.token.text
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placeholder.markdown(full_response + "▌")
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placeholder.markdown(full_response)
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return full_response
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def show_source(links) :
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