from dotenv import load_dotenv import gradio as gr import os from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, Settings from llama_index.llms.huggingface import HuggingFaceInferenceAPI from llama_index.embeddings.huggingface import HuggingFaceEmbedding from sentence_transformers import SentenceTransformer import firebase_admin from firebase_admin import db, credentials import datetime import uuid # Load environment variables load_dotenv() # authenticate to firebase cred = credentials.Certificate("redfernstech-fd8fe-firebase-adminsdk-g9vcn-0537b4efd6.json") firebase_admin.initialize_app(cred, {"databaseURL": "https://redfernstech-fd8fe-default-rtdb.firebaseio.com/"}) # Configure the Llama index settings Settings.llm = HuggingFaceInferenceAPI( model_name="meta-llama/Meta-Llama-3-8B-Instruct", tokenizer_name="meta-llama/Meta-Llama-3-8B-Instruct", context_window=3000, token=os.getenv("HF_TOKEN"), max_new_tokens=512, generate_kwargs={"temperature": 0.1}, ) Settings.embed_model = HuggingFaceEmbedding( model_name="BAAI/bge-small-en-v1.5" ) # Define the directory for persistent storage and data PERSIST_DIR = "db" PDF_DIRECTORY = 'data' # Changed to the directory containing PDFs # Ensure directories exist os.makedirs(PDF_DIRECTORY, exist_ok=True) os.makedirs(PERSIST_DIR, exist_ok=True) # Variable to store current chat conversation current_chat_history = [] def data_ingestion_from_directory(): # Use SimpleDirectoryReader on the directory containing the PDF files documents = SimpleDirectoryReader(PDF_DIRECTORY).load_data() storage_context = StorageContext.from_defaults() index = VectorStoreIndex.from_documents(documents) index.storage_context.persist(persist_dir=PERSIST_DIR) def handle_query(query): chat_text_qa_msgs = [ ( "user", """ As FernAI, your goal is to offer top-tier service and information about RedFerns Tech company. Provide concise answers based on the conversation flow. Ultimately, aim to attract users to connect with our services. Summarize responses effectively in 20-60 words without unnecessary repetition. {context_str} Question: {query_str} """ ) ] text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs) # Load index from storage storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR) index = load_index_from_storage(storage_context) # Use chat history to enhance response context_str = "" for past_query, response in reversed(current_chat_history): if past_query.strip(): context_str += f"User asked: '{past_query}'\nBot answered: '{response}'\n" query_engine = index.as_query_engine(text_qa_template=text_qa_template, context_str=context_str) answer = query_engine.query(query) if hasattr(answer, 'response'): response = answer.response elif isinstance(answer, dict) and 'response' in answer: response = answer['response'] else: response = "Sorry, I couldn't find an answer." # Update current chat history current_chat_history.append((query, response)) return response # Example usage: Process PDF ingestion from directory print("Processing PDF ingestion from directory:", PDF_DIRECTORY) data_ingestion_from_directory() # Define the function to handle predictions """def predict(message,history): response = handle_query(message) return response""" def predict(message, history): logo_html = ''' ''' response = handle_query(message) response_with_logo = f'' return response_with_logo def save_chat_message(session_id, message_data): ref = db.reference(f'/chat_history/{session_id}') # Use the session ID to save chat data ref.push().set(message_data) # Define your Gradio chat interface function (replace with your actual logic) def chat_interface(message, history): try: # Generate a unique session ID for this chat session session_id = str(uuid.uuid4()) # Process the user message and generate a response (your chatbot logic) response = handle_query(message) # Capture the message data message_data = { "sender": "user", "message": message, "response": response, "timestamp": datetime.datetime.now().isoformat() # Use a library like datetime } # Call the save function to store in Firebase with the generated session ID save_chat_message(session_id, message_data) # Return the bot response return response except Exception as e: return str(e) # Custom CSS for styling css = ''' .circle-logo { display: inline-block; width: 40px; height: 40px; border-radius: 50%; overflow: hidden; margin-right: 10px; vertical-align: middle; } .circle-logo img { width: 100%; height: 100%; object-fit: cover; } .response-with-logo { display: flex; align-items: center; margin-bottom: 10px; } footer { display: none !important; background-color: #F8D7DA; } label.svelte-1b6s6s {display: none} ''' gr.ChatInterface(chat_interface, css=css, description="FernAI", clear_btn=None, undo_btn=None, retry_btn=None, examples=['Tell me about Redfernstech?', 'Services in Redfernstech?'] ).launch()