app.py

from dotenv import load_dotenv
import gradio as gr
import os
import uvicorn
from fastapi import FastAPI, Request
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
import firebase_admin
from firebase_admin import db, credentials
import datetime
import uuid
import threading
import random

# Function to select a random name
def select_random_name():
    names = ['Clara', 'Lily']
    return random.choice(names)

# 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 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'

# 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",
            """
            You are the Clara Redfernstech chatbot. Your goal is to provide accurate, professional, and helpful answers to user queries based on the company's data. Always ensure your responses are clear and concise. Give responses within 10-15 words only.
            {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

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)

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;
  }
.svelte-1ed2p3z p {
  font-size: 24px;
  font-weight: bold;
  line-height: 1.2;
  color: #111;
  margin: 20px 0;
}
label.svelte-1b6s6s {display: none}
div.svelte-rk35yg {display: none;}
div.progress-text.svelte-z7cif2.meta-text {display: none;}
'''

app = FastAPI()

@app.get("/")
async def root():
    return {"message": "Hello"}

@app.get("/chat")
async def chat_ui(username: str, email: str):
    gr.ChatInterface(
        fn=chat_interface,
        css=css,
        description="Clara",
        clear_btn=None,
        undo_btn=None,
        retry_btn=None
    ).launch()
    return {"message": "Chat interface launched."}

if __name__ == "__main__":
    threading.Thread(target=lambda: uvicorn.run(app, host="0.0.0.0", port=8000), daemon=True).start()