File size: 5,178 Bytes
d5f01a7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 | from typing import Any
import gradio as gr
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import PyPDFLoader
import fitz
from PIL import Image
import chromadb
import re
import uuid
enable_box = gr.Textbox.update(value = None, placeholder = 'Upload your OpenAI API key',interactive = True)
disable_box = gr.Textbox.update(value = 'OpenAI API key is Set', interactive = False)
def set_apikey(api_key: str):
app.OPENAI_API_KEY = api_key
return disable_box
def enable_api_box():
return enable_box
def add_text(history, text: str):
if not text:
raise gr.Error('enter text')
history = history + [(text,'')]
return history
class my_app:
def __init__(self, OPENAI_API_KEY: str = None ) -> None:
self.OPENAI_API_KEY: str = OPENAI_API_KEY
self.chain = None
self.chat_history: list = []
self.N: int = 0
self.count: int = 0
def __call__(self, file: str) -> Any:
if self.count==0:
self.chain = self.build_chain(file)
self.count+=1
return self.chain
def chroma_client(self):
#create a chroma client
client = chromadb.Client()
#create a collecyion
collection = client.get_or_create_collection(name="my-collection")
return client
def process_file(self,file: str):
loader = PyPDFLoader(file.name)
documents = loader.load()
pattern = r"/([^/]+)$"
match = re.search(pattern, file.name)
file_name = match.group(1)
return documents, file_name
def build_chain(self, file: str):
documents, file_name = self.process_file(file)
#Load embeddings model
embeddings = OpenAIEmbeddings(openai_api_key=self.OPENAI_API_KEY)
pdfsearch = Chroma.from_documents(documents, embeddings, collection_name= file_name,)
chain = ConversationalRetrievalChain.from_llm(
ChatOpenAI(temperature=0.0, openai_api_key=self.OPENAI_API_KEY),
retriever=pdfsearch.as_retriever(search_kwargs={"k": 1}),
return_source_documents=True,)
return chain
def get_response(history, query, file):
if not file:
raise gr.Error(message='Upload a PDF')
chain = app(file)
result = chain({"question": query, 'chat_history':app.chat_history},return_only_outputs=True)
app.chat_history += [(query, result["answer"])]
app.N = list(result['source_documents'][0])[1][1]['page']
for char in result['answer']:
history[-1][-1] += char
yield history,''
def render_file(file):
doc = fitz.open(file.name)
page = doc[app.N]
#Render the page as a PNG image with a resolution of 300 DPI
pix = page.get_pixmap(matrix=fitz.Matrix(300/72, 300/72))
image = Image.frombytes('RGB', [pix.width, pix.height], pix.samples)
return image
def render_first(file):
doc = fitz.open(file.name)
page = doc[0]
#Render the page as a PNG image with a resolution of 300 DPI
pix = page.get_pixmap(matrix=fitz.Matrix(300/72, 300/72))
image = Image.frombytes('RGB', [pix.width, pix.height], pix.samples)
return image,[]
app = my_app()
with gr.Blocks() as demo:
with gr.Column():
with gr.Row():
with gr.Column(scale=0.8):
api_key = gr.Textbox(placeholder='Enter OpenAI API key', show_label=False, interactive=True).style(container=False)
with gr.Column(scale=0.2):
change_api_key = gr.Button('Change Key')
with gr.Row():
chatbot = gr.Chatbot(value=[], elem_id='chatbot').style(height=650)
show_img = gr.Image(label='Upload PDF', tool='select' ).style(height=680)
with gr.Row():
with gr.Column(scale=0.60):
txt = gr.Textbox(
show_label=False,
placeholder="Enter text and press enter",
).style(container=False)
with gr.Column(scale=0.20):
submit_btn = gr.Button('submit')
with gr.Column(scale=0.20):
btn = gr.UploadButton("📁 upload a PDF", file_types=[".pdf"]).style()
api_key.submit(
fn=set_apikey,
inputs=[api_key],
outputs=[api_key,])
change_api_key.click(
fn= enable_api_box,
outputs=[api_key])
btn.upload(
fn=render_first,
inputs=[btn],
outputs=[show_img,chatbot],)
submit_btn.click(
fn=add_text,
inputs=[chatbot,txt],
outputs=[chatbot, ],
queue=False).success(
fn=get_response,
inputs = [chatbot, txt, btn],
outputs = [chatbot,txt]).success(
fn=render_file,
inputs = [btn],
outputs=[show_img]
)
demo.queue()
demo.launch() |