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| import os | |
| import streamlit as st | |
| from langchain.embeddings.openai import OpenAIEmbeddings | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain.document_loaders.csv_loader import CSVLoader | |
| from langchain_community.document_loaders import PyPDFLoader | |
| from langchain.vectorstores import FAISS | |
| from langchain.prompts import load_prompt | |
| from langchain.text_splitter import CharacterTextSplitter | |
| from streamlit import session_state as ss | |
| import firebase_admin | |
| from firebase_admin import credentials | |
| from firebase_admin import firestore | |
| import uuid | |
| import json | |
| import time | |
| import datetime | |
| # Function to check if a string is a valid JSON | |
| def is_valid_json(data): | |
| try: | |
| json.loads(data) | |
| return True | |
| except json.JSONDecodeError: | |
| return False | |
| if "firebase_json_key" in os.environ: | |
| firebase_json_key = os.getenv("firebase_json_key") | |
| else: | |
| firebase_json_key = st.secrets["firebase_json_key"] | |
| firebase_credentials = json.loads(firebase_json_key) | |
| # Function to initialize connection to Firebase Firestore | |
| def init_connection(): | |
| cred = credentials.Certificate(firebase_credentials) | |
| firebase_admin.initialize_app(cred) | |
| return firestore.client() | |
| # Attempt to connect to Firebase Firestore | |
| try: | |
| db = init_connection() | |
| except Exception as e: | |
| st.write("Failed to connect to Firebase:", e) | |
| # Access Firebase Firestore collection | |
| if 'db' in locals(): | |
| conversations_collection = db.collection('conversations') | |
| else: | |
| st.write("Unable to access conversations collection. Firebase connection not established.") | |
| # Retrieve OpenAI API key | |
| if "OPENAI_API_KEY" in os.environ: | |
| openai_api_key = os.getenv("OPENAI_API_KEY") | |
| else: | |
| openai_api_key = st.secrets["OPENAI_API_KEY"] | |
| # Streamlit app title and disclaimer | |
| st.title("HariGPT - Hari's resume bot") | |
| st.image("images/about.jpg", use_column_width=True) | |
| with st.expander("⚠️Disclaimer"): | |
| st.write("""This bot is a LLM trained on GPT-4o-mini model to answer questions about Hari's professional background and qualifications. Your responses are recorded in a database for quality assurance and improvement purposes. Please be respectful and avoid asking personal or inappropriate questions.""") | |
| # Define file paths and load initial settings | |
| path = os.path.dirname(__file__) | |
| prompt_template = path+"/templates/template.json" | |
| prompt = load_prompt(prompt_template) | |
| faiss_index = path+"/faiss_index" | |
| data_source = path+"/data/about_hari.csv" | |
| pdf_source = path+"/data/HarikrishnaDev_DataScientist.pdf" | |
| # Function to store conversation in Firebase | |
| def store_conversation(conversation_id, user_message, bot_message, answered): | |
| timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") | |
| data = { | |
| "conversation_id": conversation_id, | |
| "timestamp": timestamp, | |
| "user_message": user_message, | |
| "bot_message": bot_message, | |
| "answered": answered | |
| } | |
| conversations_collection.add(data) | |
| # Initialize OpenAI embeddings | |
| embeddings = OpenAIEmbeddings() | |
| # Load FAISS index or create a new one if it doesn't exist | |
| if os.path.exists(faiss_index): | |
| vectors = FAISS.load_local(faiss_index, embeddings, allow_dangerous_deserialization=True) | |
| else: | |
| # Load data from PDF and CSV sources | |
| text_splitter = CharacterTextSplitter( | |
| separator="\n", | |
| chunk_size=400, | |
| chunk_overlap=40 | |
| ) | |
| pdf_loader = PyPDFLoader(pdf_source) | |
| pdf_data = pdf_loader.load_and_split(text_splitter=text_splitter) | |
| csv_loader = CSVLoader(file_path=data_source, encoding="utf-8") | |
| csv_data = csv_loader.load() | |
| data = pdf_data + csv_data | |
| # Create embeddings for the documents and save the index | |
| vectors = FAISS.from_documents(data, embeddings) | |
| vectors.save_local("faiss_index") | |
| # Initialize conversational retrieval chain | |
| retriever = vectors.as_retriever(search_type="similarity", search_kwargs={"k": 6, "include_metadata": True, "score_threshold": 0.6}) | |
| chain = ConversationalRetrievalChain.from_llm(llm=ChatOpenAI(temperature=0.5, model_name='gpt-4o-mini', openai_api_key=openai_api_key), | |
| retriever=retriever, return_source_documents=True, verbose=True, chain_type="stuff", | |
| max_tokens_limit=4097, combine_docs_chain_kwargs={"prompt": prompt}) | |
| # Function to handle conversational chat | |
| def conversational_chat(query): | |
| with st.spinner("Thinking..."): | |
| result = chain({"system": | |
| "You are a Resume Bot, a comprehensive, interactive resource for exploring Harikrishna (Harry) Dev's background, skills, and expertise. Be polite and provide answers based on the provided context only. Only answer questions relevant to Harikrishna and his work experience. Answer question if there are ONLY regarding Harikrishna Dev. If the questions is not relevant to Harikrishna, reply that you are a Resume bot. You can make up projects with the skills and projects I have if the question requests a skill set related to Machine Learning, Database management or Computer sciences.", | |
| "question": query, | |
| "chat_history": st.session_state['history']}) | |
| # Check if the result is a valid JSON | |
| if is_valid_json(result["answer"]): | |
| data = json.loads(result["answer"]) | |
| else: | |
| data = json.loads('{"answered":"false", "response":"Hmm... Something is not right. I\'m experiencing technical difficulties. Try asking your question again or ask another question about Hari\'s professional background and qualifications. Thank you for your understanding.", "questions":["What is Hari\'s professional experience?","What projects has Hari worked on?","What are Hari\'s career goals?"]}') | |
| answered = data.get("answered") | |
| response = data.get("response") | |
| questions = data.get("questions") | |
| full_response="--" | |
| # Append user query and bot response to chat history | |
| st.session_state['history'].append((query, response)) | |
| # Process the response based on the answer status | |
| if ('I am tuned to only answer questions' in response) or (response == ""): | |
| full_response = """Unfortunately, I can't answer this question. My capabilities are limited to providing information about Hari's professional background and qualifications. If you have other inquiries, I recommend reaching out to Hari on [LinkedIn](https://www.linkedin.com/in/harikrishnad1997/). I can answer questions like: \n - What is Hari's educational background? \n - Can you list Hari's professional experience? \n - What skills does Hari possess? \n""" | |
| store_conversation(st.session_state["uuid"], query, full_response, answered) | |
| else: | |
| markdown_list = "" | |
| for item in questions: | |
| markdown_list += f"- {item}\n" | |
| full_response = response + "\n\n What else would you like to know about Hari? You can ask me: \n" + markdown_list | |
| store_conversation(st.session_state["uuid"], query, full_response, answered) | |
| return(full_response) | |
| # Initialize session variables if not already present | |
| if "uuid" not in st.session_state: | |
| st.session_state["uuid"] = str(uuid.uuid4()) | |
| if "openai_model" not in st.session_state: | |
| st.session_state["openai_model"] = "gpt-4o-mini" | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [] | |
| with st.chat_message("assistant"): | |
| message_placeholder = st.empty() | |
| welcome_message = """ | |
| Welcome! I'm **Resume Bot**, a virtual assistant designed to provide insights into Harikrishna (Harry) Dev's background and qualifications. | |
| Feel free to inquire about any aspect of Hari's profile, such as his educational journey, internships, professional projects, areas of expertise in data science and AI, or his future goals. | |
| - His Master's in Business Analytics with a focus on Data Science from UTD | |
| - His track record in roles at companies like Daifuku, Flipkart, and Walmart | |
| - His proficiency in programming languages, ML frameworks, data visualization, and cloud platforms | |
| - His passion for leveraging transformative technologies to drive innovation and positive societal impact | |
| What would you like to know first? I'm ready to answer your questions in detail. | |
| """ | |
| message_placeholder.markdown(welcome_message) | |
| if 'history' not in st.session_state: | |
| st.session_state['history'] = [] | |
| # Display previous chat messages | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| # Process user input and display bot response | |
| if prompt := st.chat_input("Ask me about Hari"): | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| with st.chat_message("user"): | |
| user_input=prompt | |
| st.markdown(prompt) | |
| with st.chat_message("assistant"): | |
| message_placeholder = st.empty() | |
| full_response = "" | |
| full_response = conversational_chat(user_input) | |
| message_placeholder.markdown(full_response) | |
| st.session_state.messages.append({"role": "assistant", "content": full_response}) |