Metadata.py

import streamlit as st
import streamlit.components.v1 as stc
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

from PIL import Image
import exifread  # Extracts Meta data of images
import os
from datetime import datetime

import mutagen # Extracts Meta data of Audio
from PIL.ExifTags import TAGS, GPSTAGS
import base64
import time
from PyPDF2 import PdfReader
timestr = time.strftime("%Y%m%d-%H%M%S")



details = """
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
"""

HTML_BANNER = """
    <div style="background-color:violet;padding:10px;border-radius:10px">
    <h1 style="color:white;text-align:center;">MetaData Extractor App </h1>
    </div>
    """

def file_download(data):
    csv_file= data.to_csv()
    b64=base64.b64encode(csv_file.encode()).decode()
    new_filename="result_{}.csv".format(timestr)
    st.markdown('### 🗃️ Download csv file ')
    href=f'<a href="data:file/csv;base64,{b64}" download="{new_filename}"> Click Here! </a>'
    st.markdown(href, unsafe_allow_html=True)


    


def view_all_data():
	c.execute('SELECT * FROM filestable')
	data = c.fetchall()
	return data


def load_image(file):
    img = Image.open(file)
    return img

def get_readable_time(time):
    return datetime.fromtimestamp(time).strftime('%Y-%m-%d-%H:%M')


def get_exif(filename):
    exif = Image.open(filename).getexif()

    if exif is not None  and isinstance(exif, dict):
        for key, value in exif.items():
            name = TAGS.get(key, value)
            exif[name] = exif.pop(key)

        if 'GPSInfo' in exif:
            for key in exif['GPSInfo'].keys():
                name = GPSTAGS.get(key,key)
                exif['GPSInfo'][name] = exif['GPSInfo'].pop(key)
    return exif


def metadata():
    # st.title('Meta-Data Extractor App')
    stc.html(HTML_BANNER)
    menu=['Home','Image','Audio','Document_Files']
    choice=st.sidebar.selectbox('Menu',menu)
    if choice=='Home':
        st.image(load_image('extraction_process.png'))
        st.write(details)
        col1, col2, col3 = st.columns(3)
        with col1:
            with st.expander("Get Image Metadata 📷"):
                st.info("Image Metadata")
                st.markdown("📷")
                st.text("Upload JPEG,JPG,PNG Images")

        with col2:
            with st.expander("Get Audio Metadata 🔉"):
                st.info("Audio Metadata")
                st.markdown("🔉")
                st.text("Upload Mp3,Ogg")

        with col3:
            with st.expander("Get Document Metadata 📄📁"):
                st.info("Document Files Metadata")
                st.markdown("📄📁")
                st.text("Upload PDF,Docx")

    elif choice=='Image':
        st.subheader('Image MetaData Extractor')
        image_file = st.file_uploader("Upload Image", type=["png", "jpg", "jpeg"])
        if image_file is not None:
            with st.expander('File Stats'):
                file_details={'Filename':image_file.name,
                              'Filesize':image_file.size,
                              'Filetype':image_file.type}

                statinfo=os.stat(image_file.readable())
                statdetails={
                    'Accessed Time': get_readable_time(statinfo.st_atime),
                    'Creation Time':get_readable_time(statinfo.st_ctime),
                    'Modified Time':get_readable_time(statinfo.st_mtime)}
                full_details={
                    'Filename':image_file.name,
                     'Filesize':image_file.size,
                    'Filetype':image_file.type,
                    'Accessed Time': get_readable_time(statinfo.st_atime),
                    'Creation Time':get_readable_time(statinfo.st_ctime),
                    'Modified Time':get_readable_time(statinfo.st_mtime)
                            }
                # st.write(full_details)
                file_details_df = pd.DataFrame(
                    list(full_details.items()), columns=["Meta Tags", "Value"]
                )
                st.dataframe(file_details_df)
            c1, c2 = st.columns(2)
            with c1:
                with st.expander("View Image"):
                    img = load_image(image_file)
                    st.image(img,width=250)
            with c2:
                with st.expander("Default(JPEG)"):
                    st.info("Using PILLOW")
                    img = load_image(image_file)
                    img_details = {
                        "format": img.format,
                        "format_desc": img.format_description,
                        "filename": img.filename,
                        "size": img.size,
                        "height": img.height,
                        "width": img.width,
                        "info": img.info,
                    }
                    df_img_details = pd.DataFrame(
                        list(img_details.items()), columns=["Meta Tags", "Value"]
                    )
                    st.dataframe(df_img_details)

            c3,c4=st.columns(2)
            with c3:
                with st.expander('Using ExifRead Tool'):
                    meta_data=exifread.process_file(image_file)
                    # st.write(meta_data)
                    meta_data_df=pd.DataFrame(
                        list(meta_data.items()),columns=['Meta Data','Values'])
                    st.dataframe(meta_data_df)
            with c4:
                with st.expander('Image geo Coordinates'):
                    img_gps_details=get_exif(image_file)
                    latitude = img_gps_details.get('GPSLatitude')
                    longitude = img_gps_details.get('GPSLongitude')       
                    try:
                        gps_info = img_gps_details
                        lat=latitude
                        long=longitude
                    except:
                        gps_info = "None Found"
                    st.write(gps_info)
                    st.write(lat)
                    st.write(long)

            with st.expander('Download Results'):
                final_df=pd.concat([file_details_df,df_img_details,meta_data_df])
                st.dataframe(final_df)
                file_download(final_df)


    elif choice=='Audio':
        st.subheader('Audio MetaData Extractor')
        audio_file = st.file_uploader("Upload Audio", type=["mp3", "ogg"])

        if audio_file is not None:

            col1, col2 = st.columns(2)

            with col1:
                st.audio(audio_file.read())

            with col2:
                with st.expander("File Stats"):
                    file_details = {
                        "FileName": audio_file.name,
                        "FileSize": audio_file.size,
                        "FileType": audio_file.type,
                    }

                    st.write(file_details)

                    statinfo = os.stat(audio_file.readable())
                    stats_details = {
                        "Accessed_Time": get_readable_time(statinfo.st_atime),
                        "Creation_Time": get_readable_time(statinfo.st_ctime),
                        "Modified_Time": get_readable_time(statinfo.st_mtime),
                    }
                    st.write(stats_details)

                    file_details_combined = {
                        "FileName": audio_file.name,
                        "FileSize": audio_file.size,
                        "FileType": audio_file.type,
                        "Accessed_Time": get_readable_time(statinfo.st_atime),
                        "Creation_Time": get_readable_time(statinfo.st_ctime),
                        "Modified_Time": get_readable_time(statinfo.st_mtime),
                    }

                    df_file_details = pd.DataFrame(
                        list(file_details_combined.items()),
                        columns=["Meta Tags", "Value"],
                    )
                    st.dataframe(df_file_details)

                with st.expander('Metadata using Mutagen'):
                    meta_data=mutagen.File(audio_file)
                    meta_data_dict={str(key):str(value) for key,value in meta_data.items()}
                    meta_data_audio_df=pd.DataFrame(
                        list(meta_data_dict.items()),columns=['Tag','Values'])
                    st.dataframe(meta_data_audio_df)
            with st.expander("Download Results"):
                combined_df = pd.concat([df_file_details, meta_data_audio_df])
                st.dataframe(combined_df)
                file_download(combined_df)


    elif choice=='Document_Files':
        st.subheader('Document MetaData Extractor')
        text_file = st.file_uploader("Upload File", type=["PDF"])
        if text_file is not None:
            col1, col2 = st.columns([1, 2])

            with col1:
                with st.expander("File Stats"):
                    file_details = {
                        "FileName": text_file.name,
                        "FileSize": text_file.size,
                        "FileType": text_file.type,
                    }

                    st.write(file_details)
                    
                    statinfo = os.stat(text_file.readable())

                    stats_details = {
                        "Accessed_Time": get_readable_time(statinfo.st_atime),
                        "Creation_Time": get_readable_time(statinfo.st_ctime),
                        "Modified_Time": get_readable_time(statinfo.st_mtime),
                    }
                    st.write(stats_details)

                    # Combine All Details
                    file_details_combined = {
                        "FileName": text_file.name,
                        "FileSize": text_file.size,
                        "FileType": text_file.type,
                        "Accessed_Time": get_readable_time(statinfo.st_atime),
                        "Creation_Time": get_readable_time(statinfo.st_ctime),
                        "Modified_Time": get_readable_time(statinfo.st_mtime),
                    }

                    # Convert to DataFrame
                    df_file_details = pd.DataFrame(
                        list(file_details_combined.items()),
                        columns=["Meta Tags", "Value"],
                    )
            with col2:
                with st.expander("Metadata"):
                    pdf_file = PdfReader(text_file)
                    pdf_info = pdf_file.metadata
                    df_file_details_with_pdf = pd.DataFrame(
                        list(pdf_info.items()), columns=["Meta Tags", "Value"]
                    )

                    st.dataframe(df_file_details_with_pdf)

            with st.expander("Download Results"):
                pdf_combined_df = pd.concat([df_file_details, df_file_details_with_pdf])
                st.dataframe(pdf_combined_df)
                file_download(pdf_combined_df)