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from langchain_community.vectorstores import FAISS |
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from langchain_community.embeddings import HuggingFaceEmbeddings |
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from langchain.prompts import PromptTemplate |
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from langchain_together import Together |
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import os |
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from langchain.memory import ConversationBufferWindowMemory |
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from langchain.chains import ConversationalRetrievalChain |
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import streamlit as st |
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import time |
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st.set_page_config(page_title="zhagaramGPT") |
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col1, col2, col3 = st.columns([2,6,2]) |
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with col2: |
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st.image("logo.png") |
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st.markdown( |
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""" |
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<style> |
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div[data-baseweb="input"] input { |
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border-color: #000000; |
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} |
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margin-top: 0 !important; |
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div.stButton > button:first-child { |
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background-color: #808080; |
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color:white; |
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} |
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div.stButton > button:active { |
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background-color: #808080; |
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color : white; |
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} |
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div[data-testid="stStatusWidget"] div button { |
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display: none; |
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} |
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.reportview-container { |
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margin-top: -2em; |
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} |
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#MainMenu {visibility: hidden;} |
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.stDeployButton {display:none;} |
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footer {visibility: hidden;} |
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#stDecoration {display:none;} |
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button[title="View fullscreen"]{ |
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visibility: hidden;} |
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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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def reset_conversation(): |
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st.session_state.messages = [] |
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st.session_state.memory.clear() |
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if "messages" not in st.session_state: |
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st.session_state.messages = [] |
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if "memory" not in st.session_state: |
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st.session_state.memory = ConversationBufferWindowMemory(k=2, memory_key="chat_history",return_messages=True) |
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embeddings = HuggingFaceEmbeddings(model_name="nomic-ai/nomic-embed-text-v1",model_kwargs={"trust_remote_code":True,"revision":"289f532e14dbbbd5a04753fa58739e9ba766f3c7"}) |
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db = FAISS.load_local("ipc_vector_db", embeddings, allow_dangerous_deserialization=True) |
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db_retriever = db.as_retriever(search_type="similarity",search_kwargs={"k": 4}) |
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prompt_template = """<s>[INST]This is a chat template and As a legal chat ai specializing in Sericultural related Queries!!. |
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CONTEXT: {context} |
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CHAT HISTORY: {chat_history} |
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QUESTION: {question} |
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ANSWER: |
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</s>[INST] |
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""" |
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prompt = PromptTemplate(template=prompt_template, |
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input_variables=['context', 'question', 'chat_history']) |
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TOGETHER_AI_API= os.environ['TOGETHER_AI']="2a7c5dcdbb1049a39117ac0865c4d04008d49db31aa85a3258603817af16dbd0" |
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llm = Together( |
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model="mistralai/Mistral-7B-Instruct-v0.2", |
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temperature=0.5, |
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max_tokens=1024, |
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together_api_key=f"{TOGETHER_AI_API}" |
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) |
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qa = ConversationalRetrievalChain.from_llm( |
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llm=llm, |
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memory=st.session_state.memory, |
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retriever=db_retriever, |
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combine_docs_chain_kwargs={'prompt': prompt} |
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) |
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for message in st.session_state.messages: |
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role = message.get("role") |
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content = message.get("content") |
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with st.chat_message(role, avatar="user.svg" if role == "human" else "ai"): |
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st.write(content) |
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input_prompt = st.chat_input("message ....") |
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if input_prompt: |
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with st.chat_message("human",avatar="user.svg"): |
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st.write(input_prompt) |
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st.session_state.messages.append({"role":"human","content":input_prompt}) |
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full_response = " " |
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with st.chat_message("ai"): |
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with st.spinner("Thinking..."): |
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result = qa.invoke(input=input_prompt) |
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message_placeholder = st.empty() |
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full_response = " \n" |
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for chunk in result["answer"]: |
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full_response+=chunk |
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time.sleep(0.02) |
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message_placeholder.markdown(full_response+" β") |
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st.button('Reset All Chat ποΈ', on_click=reset_conversation) |
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st.session_state.messages.append({"role": "ai", "content": result["answer"], "avatar": "ai"}) |
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