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| import streamlit as st | |
| import os | |
| import requests | |
| from dotenv import load_dotenv # Only needed if using a .env file | |
| # Langchain and HuggingFace | |
| from langchain.vectorstores import Chroma | |
| from langchain_community.embeddings import HuggingFaceEmbeddings | |
| from langchain_groq import ChatGroq | |
| from langchain.chains import RetrievalQA | |
| # Load the .env file (if using it) | |
| load_dotenv() | |
| groq_api_key = os.getenv("GROQ_API_KEY") | |
| # Load embeddings, model, and vector store | |
| # Singleton, prevent multiple initializations | |
| def init_chain(): | |
| mojdel_kwargs = {'trust_remote_code': True} | |
| embedding = HuggingFaceEmbeddings(model_name='nomic-ai/nomic-embed-text-v1.5', model_kwargs=model_kwargs) | |
| llm = ChatGroq(groq_api_key=groq_api_key, model_name="llama3-70b-8192", temperature=0.2) | |
| vectordb = Chroma(persist_directory='updated_CSPCDB2', embedding_function=embedding) | |
| # Create chain | |
| chain = RetrievalQA.from_chain_type(llm=llm, | |
| chain_type="stuff", | |
| retriever=vectordb.as_retriever(k=5), | |
| return_source_documents=True) | |
| return chain | |
| # Streamlit app layout | |
| st.set_page_config( | |
| page_title="CSPC Citizens Charter Conversational Agent", | |
| page_icon="cspclogo.png" | |
| ) | |
| with st.sidebar: | |
| st.title('CSPCean Conversational Agent') | |
| st.subheader('Ask anything CSPC Related here!') | |
| st.markdown('''**About CSPC:** | |
| History, Core Values, Mission and Vision''') | |
| st.markdown('''**Admission & Graduation:** | |
| Apply, Requirements, Process, Graduation''') | |
| st.markdown('''**Student Services:** | |
| Scholarships, Orgs, Facilities''') | |
| st.markdown('''**Academics:** | |
| Degrees, Courses, Faculty''') | |
| st.markdown('''**Officials:** | |
| President, VPs, Deans, Admin''') | |
| st.markdown(''' | |
| Access the resources here: | |
| - [CSPC Citizen’s Charter](https://cspc.edu.ph/governance/citizens-charter/) | |
| - [About CSPC](https://cspc.edu.ph/about/) | |
| - [College Officials](https://cspc.edu.ph/college-officials/) | |
| ''') | |
| st.markdown('Team XceptionNet') | |
| # Store LLM generated responses | |
| if "messages" not in st.session_state: | |
| st.session_state.chain = init_chain() | |
| st.session_state.messages = [{"role": "assistant", "content": "How may I help you today?"}] | |
| # Function for generating response using the last three conversations | |
| def generate_response(prompt_input): | |
| # Initialize result | |
| result = '' | |
| # Prepare conversation history: get the last 3 user and assistant messages | |
| conversation_history = "" | |
| recent_messages = st.session_state.messages[-3:] # Last 3 user and assistant exchanges (each exchange is 2 messages) | |
| for message in recent_messages: | |
| conversation_history += f"{message['role']}: {message['content']}\n" | |
| # Append the current user prompt to the conversation history | |
| conversation_history += f"user: {prompt_input}\n" | |
| # Invoke chain with the truncated conversation history | |
| res = st.session_state.chain.invoke(conversation_history) | |
| # Process response (as in the original code) | |
| if res['result'].startswith('According to the provided context, '): | |
| res['result'] = res['result'][35:] | |
| res['result'] = res['result'][0].upper() + res['result'][1:] | |
| elif res['result'].startswith('Based on the provided context, '): | |
| res['result'] = res['result'][31:] | |
| res['result'] = res['result'][0].upper() + res['result'][1:] | |
| elif res['result'].startswith('According to the provided text, '): | |
| res['result'] = res['result'][34:] | |
| res['result'] = res['result'][0].upper() + res['result'][1:] | |
| elif res['result'].startswith('According to the context, '): | |
| res['result'] = res['result'][26:] | |
| res['result'] = res['result'][0].upper() + res['result'][1:] | |
| # result += res['result'] | |
| # # Process sources | |
| # result += '\n\nSources: ' | |
| # sources = [] | |
| # for source in res["source_documents"]: | |
| # sources.append(source.metadata['source'][122:-4]) # Adjust as per your source format | |
| # sources = list(set(sources)) # Remove duplicates | |
| # source_list = ", ".join(sources) | |
| # result += source_list | |
| # return result, res['result'], source_list | |
| # return result, res['result'] | |
| return res['result'] | |
| # Display chat messages | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.write(message["content"]) | |
| # User-provided prompt for input box | |
| if prompt := st.chat_input(placeholder="Ask a question..."): | |
| # Append user query to session state | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| with st.chat_message("user"): | |
| st.write(prompt) | |
| # Generate and display placeholder for assistant response | |
| with st.chat_message("assistant"): | |
| message_placeholder = st.empty() # Placeholder for response while it's being generated | |
| with st.spinner("Generating response..."): | |
| # Use conversation history when generating response | |
| response = generate_response(prompt) | |
| message_placeholder.markdown(response) # Replace placeholder with actual response | |
| st.session_state.messages.append({"role": "assistant", "content": response}) | |
| # Clear chat history function | |
| def clear_chat_history(): | |
| # Clear chat messages (reset the assistant greeting) | |
| st.session_state.messages = [{"role": "assistant", "content": "How may I help you today?"}] | |
| # Reinitialize the chain to clear any stored history (ensures it forgets previous user inputs) | |
| st.session_state.chain = init_chain() | |
| # Clear any additional session state that might be remembering user inquiries | |
| if "recent_user_messages" in st.session_state: | |
| del st.session_state["recent_user_messages"] # Clear remembered user inputs | |
| st.sidebar.button('Clear Chat History', on_click=clear_chat_history) |