from flask import Flask, request
import os
import requests
from langchain.vectorstores import Chroma
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
from InstructorEmbedding import INSTRUCTOR
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.chat_models import ChatOpenAI
import numpy
import torch
import json
import textwrap
from flask_cors import CORS
import socket;
import gradio as gr
app = Flask(__name__)
cors = CORS(app)
def get_local_ip():
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
s.connect(("8.8.8.8", 80))
return s.getsockname()[0]
def wrap_text_preserve_newlines(text, width=110):
# Split the input text into lines based on newline characters
lines = text.split('\n')
# Wrap each line individually
wrapped_lines = [textwrap.fill(line, width=width) for line in lines]
# Join the wrapped lines back together using newline characters
wrapped_text = '\n'.join(wrapped_lines)
return wrapped_text
def process_llm_response(llm_response):
response_data = {
'result': wrap_text_preserve_newlines(llm_response['result']),
'sources': []
}
print(wrap_text_preserve_newlines(llm_response['result']))
print('\n\nSources:')
for source in llm_response["source_documents"]:
print(source.metadata['source']+ "Page Number: " + str(source.metadata['page']))
response_data['sources'].append({"book": source.metadata['source'], "page": source.metadata['page']})
# return json.dumps(response_data)
return response_data
# @app.route('/question', methods=['POST'])
# def answer():
# content_type = request.headers.get('Content-Type')
# if (content_type == 'application/json'):
# data = request.json
# question = data['question']
# response = get_answer(question)
# return response
# else:
# return 'Content-Type not supported!'
ip=get_local_ip()
os.environ["OPENAI_API_KEY"] = "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"
# Embed and store the texts
# if(torch.cuda.is_available() == False):
# print("No GPU available")
# exit(1)
torch.cuda.empty_cache()
torch.max_split_size_mb = 100
instructor_embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl",
model_kwargs={"device": "cpu"})
# Supplying a persist_directory will store the embeddings on disk
persist_directory = 'db'
vectordb2 = Chroma(persist_directory=persist_directory,
embedding_function=instructor_embeddings,
)
retriever = vectordb2.as_retriever(search_kwargs={"k": 3})
vectordb2.persist()
# Set up the turbo LLM
turbo_llm = ChatOpenAI(
temperature=0,
model_name='gpt-3.5-turbo'
)
qa_chain = RetrievalQA.from_chain_type(llm=turbo_llm,
chain_type="stuff",
retriever=retriever,
return_source_documents=True)
qa_chain.combine_documents_chain.llm_chain.prompt.messages[0].prompt.template= """
Use only the following pieces of context. Answer the users question only if they are related to the context given.
If you don't know the answer, just say that you don't know, don't try to make up an answer. Make your answer very detailed and long.
Use bullet points to explain when required.
Use only text found in the context as your knowledge source for the answer.
----------------
{context}"""
def book_url(book):
if book == "BD Human Anatomy - Lower Limb, Abdomen & Pelvis (Volume 2).pdf":
return "BD+Human+Anatomy+-+Lower+Limb%2C+Abdomen+%26+Pelvis+(Volume+2).pdf"
elif book == "BD Human Anatomy - Upper Limb & Thorax (Volume 1).pdf":
return "BD+Human+Anatomy+-+Upper+Limb++Thorax+(Volume+1).pdf"
elif book == "[Richard S.Snell] Clinical Neuroanatomy (7th Ed.)":
return "%5BRichard+S.Snell%5D+Clinical+Neuroanatomy+(7th+Ed.).pdf"
elif book == "BD Chaurasia's Handbook of General Anatomy, 4th Edition.pdf":
return "BD+Chaurasia's+Handbook+of+General+Anatomy%2C+4th+Edition.pdf"
elif book == "Vishram Singh Textbook of Anatomy Upper Limb and Thorax..pdf":
return "BD+Chaurasia's+Handbook+of+General+Anatomy%2C+4th+Edition.pdf"
elif book == "Vishram Singh Textbook of Anatomy Vol 2.pdf":
return "Vishram+Singh+Textbook+of+Anatomy+Vol+2.pdf"
elif book == "BD Human Anatomy - Head, Neck & Brain (Volume 3).pdf":
return "BD+Human+Anatomy+-+Head%2C+Neck+%26+Brain+(Volume+3).pdf"
elif book == "Textbook of Clinical Neuroanatomy.pdf":
return "Textbook+of+Clinical+Neuroanatomy.pdf"
elif book == "Vishram Singh Textbook of Anatomy Vol 3.pdf":
return "Vishram+Singh+Textbook+of+Anatomy+Vol+3.pdf"
def print_array(arr):
# Convert the array to a string representation
arr_str = str(arr)
return arr_str
def html_link_generator(book, page):
bookurl = book_url(book)
url = f"https://diagrams1.s3.ap-south-1.amazonaws.com/anatomybooks/{bookurl}#page={page}"
# html = f'<iframe src="{url}" width="800" height="600"></iframe>'
# print(url)
return url
def getanswer(question):
if question=="" :
return "Please ask a question" , []
llm_response = qa_chain(question)
response = process_llm_response(llm_response)
html= html_link_generator(response["sources"][0]["book"][22:], response["sources"][0]["page"])
# html = """<iframe src="https://diagrams1.s3.ap-south-1.amazonaws.com/anatomybooks/BD+Chaurasia's+Handbook+of+General+Anatomy%2C+4th+Edition.pdf#page=40" width="800" height="600"></iframe>"""
return response["result"], response['sources']
def makevisible(source1,source2,source3):
return{
source1: gr.update(visible=True),
source2: gr.update(visible=True),
source3: gr.update(visible=True)
}
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(scale=1, min_width=600):
question = gr.Textbox(label="Question")
submitbtn = gr.Button("Submit").style(full_width=True)
answer = gr.Textbox(label="Answer", interactive=False)
sources = gr.Json(label="Sources", interactive=False)
source1 = gr.Button(label="Source 1", visible=False)
source2 = gr.Button(label="Source 2", visible=False)
source3 = gr.Button(label="Source 3", visible=False)
submitbtn.click(fn=getanswer, inputs=[question], outputs=[answer, sources], api_name="question")
# source1.click(fn=None, _js=f"""window.open('"""+"""', target="_blank");""")
# sources.change(make_source_buttons, [sources, source1, source2, source3], [source1,source2,source3])
demo.launch()