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import streamlit as st |
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from llama_index import VectorStoreIndex, SimpleDirectoryReader |
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from langchain.embeddings.huggingface import HuggingFaceEmbeddings |
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from llama_index import LangchainEmbedding, ServiceContext |
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from llama_index import StorageContext, load_index_from_storage |
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from llama_index import LLMPredictor |
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from langchain import HuggingFaceHub |
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from pathlib import Path |
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from time import sleep |
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import random |
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import string |
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import sys |
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import os |
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from dotenv import load_dotenv |
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load_dotenv() |
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st.set_page_config(page_title="Open AI Doc-Chat Assistant", layout="wide") |
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st.subheader("Open AI Doc-Chat Assistant: Life Enhancing with AI!") |
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css_file = "main.css" |
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with open(css_file) as f: |
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st.markdown("<style>{}</style>".format(f.read()), unsafe_allow_html=True) |
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st.sidebar.markdown( |
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""" |
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<style> |
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.blue-underline { |
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text-decoration: bold; |
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color: blue; |
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} |
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</style> |
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""", |
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unsafe_allow_html=True |
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) |
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st.markdown( |
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""" |
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<style> |
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[data-testid=stSidebar] [data-testid=stImage]{ |
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text-align: center; |
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display: block; |
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margin-left: auto; |
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margin-right: auto; |
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width: 50%; |
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} |
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</style> |
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""", unsafe_allow_html=True |
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) |
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HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN") |
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wechat_image= "WeChatCode.jpg" |
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documents = SimpleDirectoryReader('data').load_data() |
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embed_model = LangchainEmbedding(HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')) |
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llm_predictor = LLMPredictor(HuggingFaceHub(repo_id="HuggingFaceH4/starchat-beta", model_kwargs={"min_length":100, "max_new_tokens":1024, "do_sample":True, "temperature":0.2,"top_k":50, "top_p":0.95, "eos_token_id":49155})) |
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service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, embed_model=embed_model) |
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def generate_random_string(length): |
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letters = string.ascii_lowercase |
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return ''.join(random.choice(letters) for i in range(length)) |
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random_string = generate_random_string(20) |
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new_index = VectorStoreIndex.from_documents( |
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documents, |
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service_context=service_context, |
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) |
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new_index.storage_context.persist("random_string") |
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storage_context = StorageContext.from_defaults(persist_dir="random_string") |
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loadedindex = load_index_from_storage(storage_context=storage_context, service_context=service_context) |
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query_engine = loadedindex.as_query_engine() |
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question = st.text_input("Enter your query here:") |
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display_output_text = st.checkbox("Check AI Repsonse", key="key_checkbox", help="Check me to get AI Response.") |
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with st.sidebar: |
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st.subheader("Valuation.pdf furnished background!") |
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st.write("Disclaimer: This app is for information purpose only. NO liability could be claimed against whoever associated with this app in any manner. User should consult a qualified legal professional for legal advice.") |
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st.sidebar.markdown("Contact: [[email protected]](mailto:[email protected])") |
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st.sidebar.markdown('WeChat: <span class="blue-underline">pat2win</span>, or scan the code below.', unsafe_allow_html=True) |
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st.image(wechat_image) |
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st.sidebar.markdown('<span class="blue-underline">Life Enhancing with AI.</span>', unsafe_allow_html=True) |
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st.subheader("Enjoy chatting!") |
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if question !="" and not question.strip().isspace() and not question == "" and not question.strip() == "" and not question.isspace(): |
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if display_output_text==True: |
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with st.spinner("AI Thinking...Please wait a while to Cheers!"): |
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initial_response = query_engine.query(question) |
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temp_ai_response=str(initial_response) |
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final_ai_response=temp_ai_response.partition('<|end|>')[0] |
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st.write("AI Response:\n\n"+final_ai_response) |
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else: |
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print("Check the Checkbox to get AI Response.") |
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sys.exit() |
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else: |
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print("Please enter your question first.") |
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st.stop() |
