File size: 10,135 Bytes
89f1d44
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
dd4d3b3
89f1d44
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
dd4d3b3
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
import streamlit as st
import time
import pandas as pd
import io
from transformers import pipeline
from streamlit_extras.stylable_container import stylable_container
import plotly.express as px
import zipfile
from PyPDF2 import PdfReader
import docx
import os
from comet_ml import Experiment
import re
import numpy as np

st.set_page_config(layout="wide", page_title="Named Entity Recognition App")



# --- Configuration ---
COMET_API_KEY = os.environ.get("COMET_API_KEY")
COMET_WORKSPACE = os.environ.get("COMET_WORKSPACE")
COMET_PROJECT_NAME = os.environ.get("COMET_PROJECT_NAME")

comet_initialized = False
if COMET_API_KEY and COMET_WORKSPACE and COMET_PROJECT_NAME:
    comet_initialized = True

# --- Initialize session state ---
if 'file_upload_attempts' not in st.session_state:
    st.session_state['file_upload_attempts'] = 0

max_attempts = 10

# --- Helper function for model loading ---
@st.cache_resource
def load_ner_model():
    """Loads the pre-trained NER model and caches it."""
    return pipeline("token-classification", model="h2oai/deberta_finetuned_pii", aggregation_strategy="first")

# --- UI Elements ---
st.subheader("9-Personal Data Named Entity Recognition Web App", divider="rainbow")
st.link_button("by nlpblogs", "https://nlpblogs.com", type="tertiary")

expander = st.expander("**Important notes on the 9-Personal Data Named Entity Recognition Web App**")
expander.write('''
    
    **Named Entities:**
    This 9-Personal Data Named Entity Recognition Web App predicts nine (9) categories:
    
    1. **Account-related information**: Account name, account number, and transaction amounts 
    
    2. **Banking details**: BIC, IBAN, and Bitcoin or Ethereum addresses 
     
    3. **Personal information**: Full name, first name, middle name, last name, gender, and date of birth   
     
    4. **Contact information**: Email, phone number, and street address (including building number, city, county, state, and zip code) 
     
    5. **Job-related data**: Job title, job area, job descriptor, and job type
     
    6. **Financial data**: Credit card number, issuer, CVV, and currency information (code, name, and symbol)  
     
    7. **Digital identifiers**: IP addresses (IPv4 and IPv6), MAC addresses, and user agents 
     
    8. **Online presence**: URL, usernames, and passwords    
     
    9. **Other sensitive data**: SSN, vehicle VIN and VRM, phone IMEI, and nearby GPS coordinates 
    
    Results are presented in an easy-to-read table, visualized in an interactive tree map, pie chart, and bar chart, and are available for download along with a Glossary of tags.
    
    **How to Use:**
    Upload your .pdf or .docx file. Then, click the 'Results' button to extract and tag entities in your text data.
    
    **Usage Limits:**
     You can request results up to 10 times.
 
    **Customization:**
    To change the app's background color to white or black, click the three-dot menu on the right-hand side of your app, go to Settings and then Choose app theme, colors and fonts.
    
    **Technical issues:**
    If your connection times out, please refresh the page or reopen the app's URL.
    
    For any errors or inquiries, please contact us at info@nlpblogs.com
    
''')


     
    
with st.sidebar:
    container = st.container(border=True)
    container.write("**Named Entity Recognition (NER)** is the task of extracting and tagging entities in text data. Entities can be persons, organizations, locations, countries, products, events etc.")
    st.subheader("Related NLP Web Apps", divider="rainbow")
    st.link_button("8-Named Entity Recognition Web App", "https://nlpblogs.com/shop/named-entity-recognition-ner/8-named-entity-recognition-web-app/", type="primary")

# --- File Upload ---
upload_file = st.file_uploader("Upload your file. Accepted file formats include: .pdf, .docx", type=['pdf', 'docx'])
text = None
df = None

if upload_file is not None:
    file_extension = upload_file.name.split('.')[-1].lower()
    if file_extension == 'pdf':
        try:
            pdf_reader = PdfReader(upload_file)
            text = ""
            for page in pdf_reader.pages:
                text += page.extract_text()
            st.write("File uploaded successfully. Due to security protocols, the file content is hidden.")
        except Exception as e:
            st.error(f"An error occurred while reading PDF: {e}")
            text = None
    elif file_extension == 'docx':
        try:
            doc = docx.Document(upload_file)
            text = "\n".join([para.text for para in doc.paragraphs])
            st.write("File uploaded successfully. Due to security protocols, the file content is hidden.")
        except Exception as e:
            st.error(f"An error occurred while reading docx: {e}")
            text = None
    else:
        st.warning("Unsupported file type.")
        text = None

st.divider()

# --- Results Button and Processing Logic ---
if st.button("Results"):
    if not comet_initialized:
        st.warning("Comet ML not initialized. Check environment variables if you wish to log data.")

    if st.session_state['file_upload_attempts'] >= max_attempts:
        st.error(f"You have requested results {max_attempts} times. You have reached your daily request limit.")
        st.stop()

    if text is None:
        st.warning("Please upload a supported file (.pdf or .docx) before requesting results.")
        st.stop()

    st.session_state['file_upload_attempts'] += 1

    with st.spinner("Analyzing text...", show_time=True):
        # Load model (cached)
        model = load_ner_model()
        text_entities = model(text)
        df = pd.DataFrame(text_entities)

        # Clean and filter DataFrame
        pattern = r'[^\w\s]'
        df['word'] = df['word'].replace(pattern, '', regex=True)
        df = df.replace('', 'Unknown').dropna()

        if df.empty:
            st.warning("No entities were extracted from the uploaded text.")
            st.stop()

        if comet_initialized:
            experiment = Experiment(
                api_key=COMET_API_KEY,
                workspace=COMET_WORKSPACE,
                project_name=COMET_PROJECT_NAME,
            )
            experiment.log_parameter("input_text_length", len(text))
            experiment.log_table("predicted_entities", df)

        # --- Display Results ---
        properties = {"border": "2px solid gray", "color": "blue", "font-size": "16px"}
        df_styled = df.style.set_properties(**properties)
        st.dataframe(df_styled, use_container_width=True)

        with st.expander("See Glossary of tags"):
            st.write('''
            '**word**': ['entity extracted from your text data']
            
            '**score**': ['accuracy score; how accurately a tag has been assigned to a given entity']
            
            '**entity_group**': ['label (tag) assigned to a given extracted entity']
            
            '**start**': ['index of the start of the corresponding entity']
            
            '**end**': ['index of the end of the corresponding entity']
            ''')

        # --- Visualizations ---
        st.subheader("Tree map", divider="rainbow")
        fig_treemap = px.treemap(df, path=[px.Constant("all"), 'word', 'entity_group'],
                                 values='score', color='entity_group')
        fig_treemap.update_layout(margin=dict(t=50, l=25, r=25, b=25))
        st.plotly_chart(fig_treemap)
        if comet_initialized:
            experiment.log_figure(figure=fig_treemap, figure_name="entity_treemap")

        value_counts1 = df['entity_group'].value_counts()
        final_df_counts = value_counts1.reset_index().rename(columns={"index": "entity_group"})

        col1, col2 = st.columns(2)
        with col1:
            st.subheader("Pie Chart", divider="rainbow")
            fig_pie = px.pie(final_df_counts, values='count', names='entity_group', hover_data=['count'], labels={'count': 'count'}, title='Percentage of predicted labels')
            fig_pie.update_traces(textposition='inside', textinfo='percent+label')
            st.plotly_chart(fig_pie)
            if comet_initialized:
                experiment.log_figure(figure=fig_pie, figure_name="label_pie_chart")

        with col2:
            st.subheader("Bar Chart", divider="rainbow")
            fig_bar = px.bar(final_df_counts, x="count", y="entity_group", color="entity_group", text_auto=True, title='Occurrences of predicted labels')
            st.plotly_chart(fig_bar)
            if comet_initialized:
                experiment.log_figure(figure=fig_bar, figure_name="label_bar_chart")

        # --- Downloadable Content ---
        dfa = pd.DataFrame(
            data={
                'word': ['entity extracted from your text data'],
                'score': ['accuracy score; how accurately a tag has been assigned to a given entity'],
                'entity_group': ['label (tag) assigned to a given extracted entity'],
                'start': ['index of the start of the corresponding entity'],
                'end': ['index of the end of the corresponding entity'],
            })

        buf = io.BytesIO()
        with zipfile.ZipFile(buf, "w") as myzip:
            myzip.writestr("Summary of the results.csv", df.to_csv(index=False))
            myzip.writestr("Glossary of tags.csv", dfa.to_csv(index=False))

        with stylable_container(
            key="download_button",
            css_styles="""button { background-color: yellow; border: 1px solid black; padding: 5px; color: black; }""",
        ):
            st.download_button(
                label="Download zip file",
                data=buf.getvalue(),
                file_name="nlpblogs_ner_results.zip",
                mime="application/zip",
            )
            if comet_initialized:
                experiment.log_asset(buf.getvalue(), file_name="downloadable_results.zip")

        st.divider()
        if comet_initialized:
            experiment.end()

st.write(f"Number of times you requested results: **{st.session_state['file_upload_attempts']}/{max_attempts}**")