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import streamlit as st |
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import streamlit.components.v1 as stc |
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import pandas as pd |
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import numpy as np |
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import seaborn as sns |
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import matplotlib.pyplot as plt |
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from PIL import Image |
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import exifread |
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import os |
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from datetime import datetime |
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import mutagen |
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from PIL.ExifTags import TAGS, GPSTAGS |
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import base64 |
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import time |
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from PyPDF2 import PdfReader |
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timestr = time.strftime("%Y%m%d-%H%M%S") |
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details = """ |
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Metadata is defined as the data providing information about one or more aspects of the data; it is used to summarize basic information about data which can make tracking and working with specific data easier |
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""" |
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HTML_BANNER = """ |
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<div style="background-color:violet;padding:10px;border-radius:10px"> |
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<h1 style="color:white;text-align:center;">MetaData Extractor App </h1> |
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</div> |
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""" |
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def file_download(data): |
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csv_file= data.to_csv() |
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b64=base64.b64encode(csv_file.encode()).decode() |
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new_filename="result_{}.csv".format(timestr) |
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st.markdown('### 🗃️ Download csv file ') |
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href=f'<a href="data:file/csv;base64,{b64}" download="{new_filename}"> Click Here! </a>' |
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st.markdown(href, unsafe_allow_html=True) |
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def view_all_data(): |
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c.execute('SELECT * FROM filestable') |
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data = c.fetchall() |
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return data |
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def load_image(file): |
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img = Image.open(file) |
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return img |
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def get_readable_time(time): |
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return datetime.fromtimestamp(time).strftime('%Y-%m-%d-%H:%M') |
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def get_exif(filename): |
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exif = Image.open(filename).getexif() |
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if exif is not None and isinstance(exif, dict): |
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for key, value in exif.items(): |
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name = TAGS.get(key, value) |
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exif[name] = exif.pop(key) |
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if 'GPSInfo' in exif: |
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for key in exif['GPSInfo'].keys(): |
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name = GPSTAGS.get(key,key) |
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exif['GPSInfo'][name] = exif['GPSInfo'].pop(key) |
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return exif |
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def metadata(): |
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stc.html(HTML_BANNER) |
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menu=['Home','Image','Audio','Document_Files'] |
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choice=st.sidebar.selectbox('Menu',menu) |
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if choice=='Home': |
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st.image(load_image('extraction_process.png')) |
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st.write(details) |
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col1, col2, col3 = st.columns(3) |
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with col1: |
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with st.expander("Get Image Metadata 📷"): |
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st.info("Image Metadata") |
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st.markdown("📷") |
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st.text("Upload JPEG,JPG,PNG Images") |
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with col2: |
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with st.expander("Get Audio Metadata 🔉"): |
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st.info("Audio Metadata") |
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st.markdown("🔉") |
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st.text("Upload Mp3,Ogg") |
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with col3: |
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with st.expander("Get Document Metadata 📄📁"): |
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st.info("Document Files Metadata") |
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st.markdown("📄📁") |
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st.text("Upload PDF,Docx") |
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elif choice=='Image': |
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st.subheader('Image MetaData Extractor') |
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image_file = st.file_uploader("Upload Image", type=["png", "jpg", "jpeg"]) |
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if image_file is not None: |
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with st.expander('File Stats'): |
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file_details={'Filename':image_file.name, |
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'Filesize':image_file.size, |
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'Filetype':image_file.type} |
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statinfo=os.stat(image_file.readable()) |
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statdetails={ |
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'Accessed Time': get_readable_time(statinfo.st_atime), |
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'Creation Time':get_readable_time(statinfo.st_ctime), |
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'Modified Time':get_readable_time(statinfo.st_mtime)} |
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full_details={ |
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'Filename':image_file.name, |
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'Filesize':image_file.size, |
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'Filetype':image_file.type, |
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'Accessed Time': get_readable_time(statinfo.st_atime), |
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'Creation Time':get_readable_time(statinfo.st_ctime), |
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'Modified Time':get_readable_time(statinfo.st_mtime) |
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} |
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file_details_df = pd.DataFrame( |
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list(full_details.items()), columns=["Meta Tags", "Value"] |
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) |
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st.dataframe(file_details_df) |
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c1, c2 = st.columns(2) |
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with c1: |
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with st.expander("View Image"): |
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img = load_image(image_file) |
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st.image(img,width=250) |
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with c2: |
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with st.expander("Default(JPEG)"): |
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st.info("Using PILLOW") |
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img = load_image(image_file) |
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img_details = { |
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"format": img.format, |
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"format_desc": img.format_description, |
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"filename": img.filename, |
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"size": img.size, |
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"height": img.height, |
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"width": img.width, |
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"info": img.info, |
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} |
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df_img_details = pd.DataFrame( |
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list(img_details.items()), columns=["Meta Tags", "Value"] |
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) |
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st.dataframe(df_img_details) |
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c3,c4=st.columns(2) |
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with c3: |
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with st.expander('Using ExifRead Tool'): |
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meta_data=exifread.process_file(image_file) |
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meta_data_df=pd.DataFrame( |
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list(meta_data.items()),columns=['Meta Data','Values']) |
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st.dataframe(meta_data_df) |
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with c4: |
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with st.expander('Image geo Coordinates'): |
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img_gps_details=get_exif(image_file) |
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latitude = img_gps_details.get('GPSLatitude') |
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longitude = img_gps_details.get('GPSLongitude') |
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try: |
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gps_info = img_gps_details |
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lat=latitude |
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long=longitude |
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except: |
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gps_info = "None Found" |
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st.write(gps_info) |
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st.write(lat) |
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st.write(long) |
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with st.expander('Download Results'): |
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final_df=pd.concat([file_details_df,df_img_details,meta_data_df]) |
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st.dataframe(final_df) |
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file_download(final_df) |
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elif choice=='Audio': |
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st.subheader('Audio MetaData Extractor') |
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audio_file = st.file_uploader("Upload Audio", type=["mp3", "ogg"]) |
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if audio_file is not None: |
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col1, col2 = st.columns(2) |
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with col1: |
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st.audio(audio_file.read()) |
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with col2: |
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with st.expander("File Stats"): |
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file_details = { |
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"FileName": audio_file.name, |
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"FileSize": audio_file.size, |
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"FileType": audio_file.type, |
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} |
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st.write(file_details) |
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statinfo = os.stat(audio_file.readable()) |
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stats_details = { |
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"Accessed_Time": get_readable_time(statinfo.st_atime), |
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"Creation_Time": get_readable_time(statinfo.st_ctime), |
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"Modified_Time": get_readable_time(statinfo.st_mtime), |
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} |
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st.write(stats_details) |
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file_details_combined = { |
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"FileName": audio_file.name, |
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"FileSize": audio_file.size, |
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"FileType": audio_file.type, |
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"Accessed_Time": get_readable_time(statinfo.st_atime), |
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"Creation_Time": get_readable_time(statinfo.st_ctime), |
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"Modified_Time": get_readable_time(statinfo.st_mtime), |
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} |
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df_file_details = pd.DataFrame( |
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list(file_details_combined.items()), |
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columns=["Meta Tags", "Value"], |
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) |
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st.dataframe(df_file_details) |
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with st.expander('Metadata using Mutagen'): |
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meta_data=mutagen.File(audio_file) |
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meta_data_dict={str(key):str(value) for key,value in meta_data.items()} |
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meta_data_audio_df=pd.DataFrame( |
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list(meta_data_dict.items()),columns=['Tag','Values']) |
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st.dataframe(meta_data_audio_df) |
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with st.expander("Download Results"): |
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combined_df = pd.concat([df_file_details, meta_data_audio_df]) |
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st.dataframe(combined_df) |
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file_download(combined_df) |
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elif choice=='Document_Files': |
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st.subheader('Document MetaData Extractor') |
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text_file = st.file_uploader("Upload File", type=["PDF"]) |
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if text_file is not None: |
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col1, col2 = st.columns([1, 2]) |
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with col1: |
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with st.expander("File Stats"): |
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file_details = { |
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"FileName": text_file.name, |
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"FileSize": text_file.size, |
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"FileType": text_file.type, |
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} |
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st.write(file_details) |
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statinfo = os.stat(text_file.readable()) |
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stats_details = { |
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"Accessed_Time": get_readable_time(statinfo.st_atime), |
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"Creation_Time": get_readable_time(statinfo.st_ctime), |
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"Modified_Time": get_readable_time(statinfo.st_mtime), |
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} |
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st.write(stats_details) |
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file_details_combined = { |
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"FileName": text_file.name, |
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"FileSize": text_file.size, |
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"FileType": text_file.type, |
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"Accessed_Time": get_readable_time(statinfo.st_atime), |
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"Creation_Time": get_readable_time(statinfo.st_ctime), |
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"Modified_Time": get_readable_time(statinfo.st_mtime), |
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} |
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df_file_details = pd.DataFrame( |
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list(file_details_combined.items()), |
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columns=["Meta Tags", "Value"], |
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) |
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with col2: |
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with st.expander("Metadata"): |
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pdf_file = PdfReader(text_file) |
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pdf_info = pdf_file.metadata |
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df_file_details_with_pdf = pd.DataFrame( |
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list(pdf_info.items()), columns=["Meta Tags", "Value"] |
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) |
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st.dataframe(df_file_details_with_pdf) |
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with st.expander("Download Results"): |
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pdf_combined_df = pd.concat([df_file_details, df_file_details_with_pdf]) |
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st.dataframe(pdf_combined_df) |
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file_download(pdf_combined_df) |
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