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import os
import streamlit as st
from dotenv import load_dotenv
from langchain_core.messages import AIMessage, HumanMessage
from langchain_community.llms import HuggingFaceEndpoint
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
import fitz # PyMuPDF
load_dotenv()
api_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
# Define the repository ID and task
repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
task = "text-generation"
# App config
st.set_page_config(page_title="Yatra Sevak.AI", page_icon="🌍")
st.title("Yatra Sevak.AI ✈️")
# Define the template outside the function
template = """
You are a travel assistant chatbot your name is Yatra Sevak.AI designed to help users plan their trips and provide travel-related information. Here are some scenarios you should be able to handle:
1. Destination Information: Provide information about popular travel destinations. Offer insights on attractions, local cuisine, cultural highlights, weather conditions, and best times to visit.
2. Travel Tips: Offer practical travel tips and advice. Topics may include packing essentials, visa requirements, currency exchange, local customs, and safety tips.
3. Local Attractions: Suggest local attractions and points of interest based on the user's destination. Highlight must-see landmarks, museums, parks, and recreational activities.
4. Customer Service: Address customer service inquiries and provide assistance with travel-related issues.
5. Online Marketing & Sales Associate: Act as an Online Marketing & Sales Associate and try to suggest our services to the user. If you think that the user can be converted to a client, then ask for their contact information and also give them our contact information.
Please ensure responses are informative, accurate, and tailored to the user's queries and preferences. Use natural language to engage users and provide a seamless experience throughout their travel planning journey.
Chat history:
{chat_history}
User question:
{user_question}
"""
pdf_path = "YatraAI_Travel_Plans.pdf"
def extract_text_from_pdf(pdf_path):
doc = fitz.open(pdf_path)
text = ""
for page in doc:
text += page.get_text()
return text
pdf_text = extract_text_from_pdf(pdf_path)
# Function to get a response from the model
def get_response(user_query, chat_history):
# Initialize the Hugging Face Endpoint
llm = HuggingFaceEndpoint(
huggingfacehub_api_token=api_token,
repo_id=repo_id,
task=task
)
# Include the PDF content in the prompt
prompt_with_pdf = f"{template}\n\nCompany Info and Travel Plans:\n{pdf_text}"
chain = ChatPromptTemplate.from_template(prompt_with_pdf) | llm | StrOutputParser()
response = chain.invoke({
"chat_history": chat_history,
"user_question": user_query,
})
return response
# Initialize session state
if "chat_history" not in st.session_state:
st.session_state.chat_history = [
AIMessage(content="Hello, I am Yatra Sevak.AI. How can I help you?"),
]
# Display chat history
for message in st.session_state.chat_history:
if isinstance(message, AIMessage):
with st.chat_message("AI"):
st.write(message.content)
elif isinstance(message, HumanMessage):
with st.chat_message("Human"):
st.write(message.content)
# User input
user_query = st.chat_input("Type your message here...")
if user_query is not None and user_query != "":
st.session_state.chat_history.append(HumanMessage(content=user_query))
with st.chat_message("Human"):
st.markdown(user_query)
response = get_response(user_query, st.session_state.chat_history)
# Remove any unwanted prefixes from the response
response = response.replace("AI response:", "").replace("chat response:", "").replace("bot response:", "").strip()
with st.chat_message("AI"):
st.write(response)
st.session_state.chat_history.append(AIMessage(content=response))
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