Spaces:
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Sleeping
Syed Junaid Iqbal
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β’
787200f
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Parent(s):
a4ad0a9
Upload app.py
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app.py
ADDED
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import streamlit as st
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from langchain.llms import LlamaCpp
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from langchain.memory import ConversationBufferMemory
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from langchain.chains import RetrievalQA
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from langchain.embeddings import FastEmbedEmbeddings
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from langchain.vectorstores import Chroma
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from langchain.callbacks.manager import CallbackManager
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from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
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from langchain import hub
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def init_retriever():
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"""
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Initialize and return the retriever function
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"""
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callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
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llm = LlamaCpp(model_path="./models/llama-2-13b-chat.Q4_K_S.gguf",
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n_ctx=4000,
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max_tokens=4000,
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f16_kv=True,
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callback_manager=callback_manager,
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verbose=True)
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embeddings = FastEmbedEmbeddings(model_name="BAAI/bge-small-en-v1.5", cache_dir="./embedding_model/")
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db = Chroma(persist_directory="./vectordb/", embedding_function=embeddings)
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rag_prompt_llama = hub.pull("rlm/rag-prompt-llama")
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qa_chain = RetrievalQA.from_chain_type(
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llm,
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retriever=db.as_retriever(),
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chain_type_kwargs={"prompt": rag_prompt_llama},
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)
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qa_chain.callback_manager = callback_manager
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qa_chain.memory = ConversationBufferMemory()
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return qa_chain
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# Check if retriever is already initialized in the session state
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if "retriever" not in st.session_state:
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st.session_state.retriever = init_retriever()
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# Function to apply rounded edges using CSS
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def add_rounded_edges(image_path="./randstad_featuredimage.png", radius=30):
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st.markdown(
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f'<style>.rounded-img{{border-radius: {radius}px; overflow: hidden;}}</style>',
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unsafe_allow_html=True,
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)
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st.image(image_path, use_column_width=True, output_format='auto')
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# add side bar
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with st.sidebar:
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# add Randstad logo
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add_rounded_edges()
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st.title("π¬ HR Chatbot")
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st.caption("π A chatbot powered by Local LLM")
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clear = False
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# Add clear chat button
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if st.button("Clear Chat History"):
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clear = True
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st.session_state.messages = []
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if "messages" not in st.session_state:
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st.session_state.messages = [{"role": "assistant", "content": "How can I help you?"}]
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for msg in st.session_state.messages:
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st.chat_message(msg["role"]).write(msg["content"])
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if prompt := st.chat_input():
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st.session_state.messages.append({"role": "user", "content": prompt})
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st.chat_message("user").write(prompt)
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chain = st.session_state.retriever
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if clear:
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chain.clean()
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msg = chain.run(st.session_state.messages)
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st.session_state.messages.append({"role": "assistant", "content": msg})
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st.chat_message("assistant").write(msg)
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