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from langchain import HuggingFaceHub, LLMChain, PromptTemplate |
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from langchain.memory import ConversationBufferWindowMemory |
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from langchain.embeddings.openai import OpenAIEmbeddings |
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from langchain.chat_models import ChatOpenAI |
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from langchain.chains import ConversationalRetrievalChain |
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from langchain.document_loaders.csv_loader import CSVLoader |
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from langchain.vectorstores import FAISS |
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import tempfile |
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from streamlit_chat import message |
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import streamlit as st |
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import os |
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import re |
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import sys |
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import pandas as pd |
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def extract_text_from_html(html): |
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cleanr = re.compile('<.*?>') |
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cleantext = re.sub(cleanr, '', html) |
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return cleantext.strip() |
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def conversational_chat(query): |
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output = llm_chain.predict(human_input=query) |
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return extract_text_from_html(output) |
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user_api_key = st.sidebar.text_input( |
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label="#### Your HuggingFace API key 👇", |
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placeholder="Paste your HuggingGace API key, sk-", |
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type="password") |
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if user_api_key is not None and user_api_key.strip() != "": |
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repo_id = "tiiuae/falcon-7b-instruct" |
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template = """ |
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Your custon promp |
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{history} |
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Me:{human_input} |
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Jack: |
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""" |
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prompt = PromptTemplate( |
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input_variables=["history", "human_input"], |
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template=template |
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) |
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llm_chain = LLMChain( |
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llm=HuggingFaceHub(huggingfacehub_api_token=user_api_key, repo_id="tiiuae/falcon-7b-instruct", model_kwargs={"temperature": 0.2}), |
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prompt=prompt, |
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verbose=True, |
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memory=ConversationBufferWindowMemory(k=2) |
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) |
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if 'history' not in st.session_state: |
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st.session_state['history'] = [] |
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if 'generated' not in st.session_state: |
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st.session_state['generated'] = ["Hello ! Ask me anything about " + " 🤗"] |
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if 'past' not in st.session_state: |
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st.session_state['past'] = ["Hey ! 👋"] |
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response_container = st.container() |
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container = st.container() |
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with container: |
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with st.form(key='my_form', clear_on_submit=True): |
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user_input = st.text_input("Query:", placeholder="Lets talk about something General", key='input') |
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submit_button = st.form_submit_button(label='Send') |
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if submit_button and user_input: |
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output = conversational_chat(user_input) |
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st.session_state['past'].append(user_input) |
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st.session_state['generated'].append(output) |
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if st.session_state['generated']: |
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with response_container: |
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for i in range(len(st.session_state['generated'])): |
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message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="big-smile") |
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message(st.session_state["generated"][i], key=str(i), avatar_style="thumbs") |
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else: |
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st.text("Please enter your HuggingFace API key above.") |
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