import streamlit as st
from langchain.prompts import PromptTemplate
from langchain_community.llms import CTransformers
from src.helper import download_hf_embeddings, text_split, download_hf_model
from langchain_community.vectorstores import Pinecone as LangchainPinecone
import os
from dotenv import load_dotenv
from src.prompt import prompt_template
from langchain.chains import RetrievalQA
import time
from pinecone import Pinecone
# Load environment variables
load_dotenv()
PINECONE_API_KEY = os.environ.get('PINECONE_API_KEY')
index_name = "medicure-chatbot"
# Set page configuration
st.set_page_config(page_title="Medical Chatbot", page_icon="🏥", layout="wide")
# Custom CSS for styling
st.markdown("""
""", unsafe_allow_html=True)
# Initialize session state for chat history
if 'chat_history' not in st.session_state:
st.session_state.chat_history = []
# Header
st.title("🏥 Medical Chatbot")
# Initialize the chatbot components
@st.cache_resource
def initialize_chatbot():
embeddings = download_hf_embeddings()
# model_name_or_path = "TheBloke/Llama-2-7B-Chat-GGML"
# model_basename = "llama-2-7b-chat.ggmlv3.q4_0.bin"
# model_path = download_hf_model(model_name_or_path, model_basename)
model_path = "TheBloke/Llama-2-7B-Chat-GGML"
llm = CTransformers(model=model_path,
model_type="llama",
config={'max_new_tokens': 512,
'temperature': 0.8})
# initiaize pinecone
pc = Pinecone(api_key=PINECONE_API_KEY)
index = pc.Index(index_name)
PROMPT = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
chain_type_kwargs = {"prompt": PROMPT}
docsearch = LangchainPinecone(index, embeddings.embed_query, "text")
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=docsearch.as_retriever(search_kwargs={'k': 2}),
return_source_documents=True,
chain_type_kwargs=chain_type_kwargs)
return qa
qa = initialize_chatbot()
# Chat interface
user_input = st.text_input("Ask your medical question:")
if st.button("Send", key="send"):
if user_input:
with st.spinner("Thinking..."):
result = qa({"query": user_input})
response = result["result"]
st.session_state.chat_history.append(("You", user_input))
st.session_state.chat_history.append(("Bot", response))
# Display chat history
st.subheader("Chat History")
for role, message in st.session_state.chat_history:
if role == "You":
st.markdown(f"**You:** {message}")
else:
st.markdown(f"**Bot:** {message}")
# Animated loading for visual appeal
def load_animation():
with st.empty():
for i in range(3):
for j in ["⋅", "⋅⋅", "⋅⋅⋅", "⋅⋅⋅⋅"]:
st.write(f"Loading{j}")
time.sleep(0.2)
st.write("")
# Footer with social links
st.markdown("""
""", unsafe_allow_html=True)
# Load Font Awesome for icons
st.markdown('', unsafe_allow_html=True)