Create app.py

app.py

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+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}**")
+