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import gradio as gr | |
import warnings | |
import json | |
#import openai | |
import os | |
import tiktoken | |
import gradio as gr | |
from langchain import OpenAI, PromptTemplate | |
from langchain.chains.summarize import load_summarize_chain | |
from langchain.document_loaders import PyPDFLoader | |
warnings.filterwarnings('ignore') | |
#openai.api_key = "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" | |
def num_tokens_from_string(string: str, encoding_name: str) -> int: | |
"""Returns the number of tokens in a text string.""" | |
encoding = tiktoken.get_encoding(encoding_name) | |
num_tokens = len(encoding.encode(string)) | |
return num_tokens | |
num_tokens_from_string("tiktoken is great!", "cl100k_base") | |
llm = OpenAI(temperature=0) | |
def summarize_pdf(pdf_file_path): | |
loader = PyPDFLoader(pdf_file_path) | |
docs = loader.load_and_split() | |
chain = load_summarize_chain(llm, chain_type="map_reduce") | |
summary = chain.run(docs) | |
return summary | |
#summarize = summarize_pdf("Assignment 1 - AIDI.pdf") | |
#summarize | |
input_pdf_path = gr.components.Textbox(label="Provide the PDF file path") | |
output_summary = gr.components.Textbox(label="Summary") | |
interface = gr.Interface( | |
fn=summarize_pdf, | |
inputs=input_pdf_path, | |
outputs=output_summary, | |
title="PDF Summarizer", | |
description="Provide PDF file path to get the summary.", | |
).launch(share=True) |