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from langchain_core.prompts import PromptTemplate
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_community.llms.ctransformers import CTransformers
from langchain.chains.retrieval_qa.base import RetrievalQA
from langchain_community.llms import HuggingFaceHub
from langchain.document_loaders import PyPDFLoader
from langchain.document_loaders import PyPDFDirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
from langchain.prompts import PromptTemplate
from langchain.chains import create_retrieval_chain
from langchain.chains import RetrievalQA
from langchain.chains.combine_documents import create_stuff_documents_chain
import os
import streamlit as st
import fitz # PyMuPDF
from PIL import Image
import io
DB_FAISS_PATH = 'vectorstores/'
pdf_path = 'Oxford/Oxford-psychiatric-handbook-1-760.pdf'
# custom_prompt_template = '''use the following pieces of information to answer the user's questions.
# If you don't know the answer, please just say that don't know the answer, don't try to make uo an answer.
# Context : {context}
# Question : {question}
# only return the helpful answer below and nothing else.
# '''
custom_prompt_template = prompt_template="""
Use the following piece of context to answer the question asked.
Please try to provide the answer only based on the context
{context}
Question:{question}
"""
def set_custom_prompt():
"""
Prompt template for QA retrieval for vector stores
"""
prompt = PromptTemplate(template = custom_prompt_template,
input_variables = ['context','question'])
return prompt
os.environ['HUGGINGFACEHUB_API_TOKEN'] = os.getenv('HUGGINGFACEHUB_API_TOKEN')
def load_llm():
# llm = CTransformers(
# model = 'TheBloke/Llama-2-7B-Chat-GGML',
# model_type = 'llama',
# max_new_token = 512,
# temperature = 0.5
# )
llm = HuggingFaceHub(
repo_id = "mistralai/Mistral-7B-v0.1",
huggingfacehub_api_token=os.getenv('HUGGINGFACEHUB_API_TOKEN'),
model_kwargs = {'temperature': 0.1, "max_length": 500}
)
return llm
def retrieval_qa_chain(llm,prompt,db):
qa_chain = RetrievalQA.from_chain_type(
llm = llm,
chain_type = 'stuff',
retriever = db.as_retriever(search_type = 'similarity',search_kwargs = {'k': 3}),
return_source_documents = True,
chain_type_kwargs = {'prompt': prompt}
)
return qa_chain
def qa_bot():
embeddings = HuggingFaceBgeEmbeddings(model_name = 'BAAI/bge-small-en-v1.5',#'sentence-transformers/all-MiniLM-L6-v2',
model_kwargs = {'device':'cpu'},
encode_kwargs = {'normalize_embeddings': True})
db = FAISS.load_local(DB_FAISS_PATH, embeddings, allow_dangerous_deserialization=True)
llm = load_llm()
qa_prompt = set_custom_prompt()
qa = retrieval_qa_chain(llm,qa_prompt, db)
return qa
def final_result(query):
qa_result = qa_bot()
response = qa_result({'query' : query})
return response
def get_pdf_page_as_image(pdf_path, page_number):
document = fitz.open(pdf_path)
page = document.load_page(page_number)
pix = page.get_pixmap()
img = Image.open(io.BytesIO(pix.tobytes()))
return img
# Streamlit webpage title
st.title('Medical Chatbot')
# User input
user_query = st.text_input("Please enter your question:")
# Button to get answer
if st.button('Get Answer'):
if user_query:
# Call the function from your chatbot script
response = final_result(user_query)
if response:
# Displaying the response
st.write("### Answer")
st.write(response['result'])
# Displaying source document details if available
if 'source_documents' in response:
st.write("### Source Document Information")
for doc in response['source_documents']:
# Retrieve and format page content by replacing '\n' with new line
formatted_content = doc.page_content.replace("\\n", "\n")
st.write("#### Document Content")
st.text_area(label="Page Content", value=formatted_content, height=300)
# Retrieve source and page from metadata
source = doc.metadata['source']
page = doc.metadata['page']
st.write(f"Source: {source}")
st.write(f"Page Number: {page+1}")
# Display the PDF page as an image
#source = r"{source}"
pdf_page_image = get_pdf_page_as_image(pdf_path, page)
st.image(pdf_page_image, caption=f"Page {page+1} from {source}")
else:
st.write("Sorry, I couldn't find an answer to your question.")
else:
st.write("Please enter a question to get an answer.")
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