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import os
import shutil
from flask import Flask, render_template, request, jsonify
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 huggingface_hub import InferenceClient
# Ensure HF_TOKEN is set
HF_TOKEN = os.getenv("HF_TOKEN")
if not HF_TOKEN:
raise ValueError("HF_TOKEN environment variable not set.")
repo_id = "meta-llama/Meta-Llama-3-8B-Instruct"
llm_client = InferenceClient(
model=repo_id,
token=HF_TOKEN,
)
# Configure Llama index settings
Settings.llm = HuggingFaceInferenceAPI(
model_name=repo_id,
tokenizer_name=repo_id,
context_window=3000,
token=HF_TOKEN,
max_new_tokens=512,
generate_kwargs={"temperature": 0.1},
)
Settings.embed_model = HuggingFaceEmbedding(
model_name="BAAI/bge-small-en-v1.5"
)
PERSIST_DIR = "db"
PDF_DIRECTORY = 'data'
# Ensure directories exist
os.makedirs(PDF_DIRECTORY, exist_ok=True)
os.makedirs(PERSIST_DIR, exist_ok=True)
chat_history = []
current_chat_history = []
def data_ingestion_from_directory():
# Clear previous data by removing the persist directory
if os.path.exists(PERSIST_DIR):
shutil.rmtree(PERSIST_DIR) # Remove the persist directory and all its contents
# Recreate the persist directory after removal
os.makedirs(PERSIST_DIR, exist_ok=True)
# Load new documents from the directory
new_documents = SimpleDirectoryReader(PDF_DIRECTORY).load_data()
# Create a new index with the new documents
index = VectorStoreIndex.from_documents(new_documents)
# Persist the new index
index.storage_context.persist(persist_dir=PERSIST_DIR)
def handle_query(query):
chat_text_qa_msgs = [
(
"user",
"""
You are the Taj Hotel chatbot and your name is Taj Hotel Helper. Your goal is to provide accurate, professional, and helpful answers to user queries based on the given Taj hotel's data. Always ensure your responses are clear and concise. Give response within 10-15 words only. You need to give an answer in the same language used by the user.
{context_str}
Question:
{query_str}
"""
)
]
text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
index = load_index_from_storage(storage_context)
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)
print(query)
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."
current_chat_history.append((query, response))
return response
app = Flask(__name__)
# Data ingestion
data_ingestion_from_directory()
# Generate Response
def generate_response(query):
try:
# Call the handle_query function to get the response
bot_response = handle_query(query)
return bot_response
except Exception as e:
return f"Error fetching the response: {str(e)}"
# Route for the homepage
@app.route('/')
def index():
return render_template('index.html')
# Route to handle chatbot messages
@app.route('/chat', methods=['POST'])
def chat():
try:
user_message = request.json.get("message")
if not user_message:
return jsonify({"response": "Please say something!"})
bot_response = generate_response(user_message)
return jsonify({"response": bot_response})
except Exception as e:
return jsonify({"response": f"An error occurred: {str(e)}"})
if __name__ == '__main__':
app.run(debug=True) |