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from typing import Any
import gradio as gr
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain
from langchain_openai import ChatOpenAI
from langchain_community.document_loaders import PyMuPDFLoader
import fitz
from PIL import Image
import os
import re
import openai
openai.api_key = "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"
def add_text(history, text: str):
if not text:
raise gr.Error("Enter text")
history = history + [(text, "")]
return history
class MyApp:
def __init__(self) -> 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 process_file(self, file: str):
loader = PyMuPDFLoader(file.name)
documents = loader.load()
pattern = r"/([^/]+)$"
match = re.search(pattern, file.name)
try:
file_name = match.group(1)
except:
file_name = os.path.basename(file)
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 150 DPI
pix = page.get_pixmap(dpi=150)
image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
return image
def purge_chat_and_render_first(file):
print("purge_chat_and_render_first")
# Purges the previous chat session so that the bot has no concept of previous documents
app.chat_history = []
app.count = 0
# Use PyMuPDF to render the first page of the uploaded document
doc = fitz.open(file.name)
page = doc[0]
# Render the page as a PNG image with a resolution of 150 DPI
pix = page.get_pixmap(dpi=150)
image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
return image, []
app = MyApp()
with gr.Blocks() as demo:
with gr.Column():
with gr.Row():
with gr.Column(scale=2):
with gr.Row():
chatbot = gr.Chatbot(value=[], elem_id="chatbot")
with gr.Row():
txt = gr.Textbox(
show_label=False,
placeholder="Enter text and press submit",
scale=2
)
submit_btn = gr.Button("Submit", scale=1)
with gr.Column(scale=1):
with gr.Row():
show_img = gr.Image(label="Upload PDF")
with gr.Row():
btn = gr.UploadButton("📁 Upload a PDF", file_types=[".pdf"])
btn.upload(
fn=purge_chat_and_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()
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