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import streamlit as st
import os
import pandas as pd
from PyPDF2 import PdfReader
import openai
from collections import defaultdict
from io import StringIO
from pdfminer.high_level import extract_text
import json
from openai import OpenAI
import re
# 1. Initialization
api_key = "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"
openai.api_key = api_key
client = OpenAI(api_key=api_key)
pdf_folder = "pdf"
st.title("Mahkamah Agung: NER & Summarization of Legal Documents")
#---------------------PDF OVERVIEW----------------------
st.subheader("PDF Folder Overview")
def get_pdf_details(folder_path):
pdf_details = []
for filename in os.listdir(folder_path):
if filename.lower().endswith('.pdf'):
pdf_path = os.path.join(folder_path, filename)
try:
with open(pdf_path, "rb") as file:
pdf_reader = PdfReader(file)
page_count = len(pdf_reader.pages)
pdf_details.append({"Filename": filename, "Page Count": page_count})
except Exception as e:
st.warning(f"Could not read {filename}: {str(e)}")
return pdf_details
pdf_list = get_pdf_details(pdf_folder)
pdf_df = pd.DataFrame(pdf_list)
if not pdf_df.empty:
with st.expander('PDF Overview'):
st.dataframe(pdf_df)
else:
st.warning("No PDFs found in the specified folder.")
#---------------------PDF SEARCH AND EXTRACT----------------------
st.subheader("PDF to Text Conversion")
# Function to read and extract text from a PDF using PdfReader
def extract_text_from_pdf_pypdf2(pdf_path):
text = ""
with open(pdf_path, "rb") as file:
pdf_reader = PdfReader(file)
for page in pdf_reader.pages:
page_text = page.extract_text()
if page_text:
text += page_text
return text
# Function to read and extract text from a PDF using pdfminer
def extract_text_from_pdf_pdfminer(pdf_path):
# Extract text using pdfminer.six
try:
text = extract_text(pdf_path)
except Exception as e:
st.error(f"Error extracting text from {pdf_path}: {str(e)}")
text = ""
return text
pdf_files = [f for f in os.listdir(pdf_folder) if f.lower().endswith('.pdf')]
search_query = st.text_input("Search for a PDF")
filtered_pdfs = [pdf for pdf in pdf_files if search_query.lower() in pdf.lower()]
if filtered_pdfs:
selected_pdf = st.selectbox("Select a PDF to convert to text", filtered_pdfs)
else:
st.warning("No PDFs found matching your search.")
if st.button("analyze The Document"):
pdf_path = os.path.join(pdf_folder, selected_pdf)
extracted_text = extract_text_from_pdf_pdfminer(pdf_path)
# Display the extracted text
if extracted_text:
with st.expander('Extracted Text'):
st.text_area("Extracted Text", value=extracted_text, height=300)
else:
st.warning("No text extracted. The PDF might contain images or other non-text content.")
# template = """
#
# # Anda adalah seorang hakim agung di Mahkamah Agung di Indonesia. Dari hasil putusan dibawah ini berikan aku kesimpulannya:
# {}
# variabel yang harus ada adalah sebagai berikut: presiding judge, member judge, clerk, ruling, other rulings, note of ruling, date of deliberation, date read out, type of judicial institution, date of register, judicial institution, case_number, court, defendants.name, defendants.place_of_birth, defendants.date_of_birth, defendants.age, defendants.gender, defendants.nationality, defendants.religion, defendants.occupation, charges.article, charges.offense, verdict.sentence, verdict.assets_confiscated.description, verdict.assets_confiscated.weight, fine dan conclusion
# # """
template = """
# Anda Adalah Seorang Hakim Agung Di Mahkamah Agung Di Indonesia. Berdasarkan Putusan Di Bawah Ini, Berikan Kesimpulannya:
{}
Variabel Yang Harus Ada Adalah Sebagai Berikut: Hakim Ketua, Hakim Anggota, Panitera, Putusan, Putusan Lainnya, Catatan Putusan, Tanggal Musyawarah, Tanggal Pembacaan, Jenis Institusi Yudisial, Tanggal Pendaftaran, Institusi Yudisial, Nomor Kasus, Pengadilan, Terdakwa.Nama, Terdakwa.Tempat_Lahir, Terdakwa.Tanggal_Lahir, Terdakwa.Usia, Terdakwa.Jenis_Kelamin, Terdakwa.Kebangsaan, Terdakwa.Agama, Terdakwa.Pekerjaan, Pasal_Dakwaan, Pelanggaran_Dakwaan, Vonis.Hukuman, Vonis.Atribut_Disita.Deskripsi, Vonis.Atribut_Disita.Berat, Denda, Dan Kesimpulan.
# """
#---------------------NER & SUMMARIZATION----------------------
response = client.chat.completions.create(
model="gpt-3.5-turbo-0125",
response_format={ "type": "json_object" },
messages=[
{"role": "system", "content": "You are a helpful assistant designed to output JSON."},
{"role": "user", "content": template.format(extracted_text)}
]
)
data= json.loads(response.choices[0].message.content)
df = pd.json_normalize(data)
df=df.T
df.columns = ["Kesimpulan Putusan"]
st.dataframe(df)
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