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 json
+import plotly.express as px
+from PyPDF2 import PdfReader
+import docx
+import zipfile
+from gliner import GLiNER
+
+
+
+st.subheader("8-Named Entity Recognition Web App", divider = "red")
+st.link_button("by nlpblogs", "https://nlpblogs.com", type = "tertiary")
+
+expander = st.expander("**Important notes on the 8-Named Entity Recognition Web App**")
+expander.write('''
+    
+    **Named Entities:**
+    This 8-Named Entity Recognition Web App predicts eight (8) labels (“person”, “country”, “city”, “organization”, “date”, “money”, “percent value”, “position”). 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 = "red")
+    st.link_button("14-Named Entity Recognition Web App", "https://nlpblogs.com/shop/named-entity-recognition-ner/14-named-entity-recognition-web-app/", type = "primary")
+
+
+
+   
+
+    
+    
+       
+
+if 'file_upload_attempts' not in st.session_state:
+    st.session_state['file_upload_attempts'] = 0
+
+max_attempts = 10
+
+
+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("Due to security protocols, the file content is hidden.")
+        except Exception as e:
+            st.error(f"An error occurred while reading PDF: {e}")
+    elif file_extension == 'docx':
+        try:
+            doc = docx.Document(upload_file)
+            text = "\n".join([para.text for para in doc.paragraphs])
+            st.write("Due to security protocols, the file content is hidden.")
+        except Exception as e:
+            st.error(f"An error occurred while reading docx: {e}")
+    else:
+        st.warning("Unsupported file type.")
+    
+        st.stop()
+
+
+        
+
+
+
+
+
+if st.button("Results"):
+    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()
+    st.session_state['file_upload_attempts'] += 1
+    
+    
+    with st.spinner('Wait for it...', show_time=True):
+        time.sleep(5)
+        model = GLiNER.from_pretrained("xomad/gliner-model-merge-large-v1.0")
+        labels = ["person", "country", "city", "organization", "date", "money", "percent value", "position"]
+        entities = model.predict_entities(text, labels)
+        df = pd.DataFrame(entities)
+            
+        properties = {"border": "2px solid gray", "color": "blue", "font-size": "16px"}
+        df_styled = df.style.set_properties(**properties)
+        st.dataframe(df_styled)
+        with st.expander("See Glossary of tags"):
+            st.write('''
+            '**text**': ['entity extracted from your text data'] 
+            
+            '**score**': ['accuracy score; how accurately a tag has been assigned to a given entity']
+            
+            '**label**': ['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']
+        
+    ''')
+        if df is not None:
+            fig = px.treemap(df, path=[px.Constant("all"), 'text', 'label'],
+                                 values='score', color='label')
+            fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))
+            st.subheader("Tree map", divider = "red")
+            st.plotly_chart(fig)
+        if df is not None:
+            value_counts1 = df['label'].value_counts()
+            df1 = pd.DataFrame(value_counts1)
+            final_df = df1.reset_index().rename(columns={"index": "label"})
+            col1, col2 = st.columns(2)
+            with col1:
+                fig1 = px.pie(final_df, values='count', names='label', hover_data=['count'], labels={'count': 'count'}, title='Percentage of predicted labels')
+                fig1.update_traces(textposition='inside', textinfo='percent+label')
+                st.subheader("Pie Chart", divider = "red")
+                st.plotly_chart(fig1)
+            with col2:
+                fig2 = px.bar(final_df, x="count", y="label", color="label", text_auto=True, title='Occurrences of predicted labels')
+                st.subheader("Bar Chart", divider = "red")
+                st.plotly_chart(fig2)
+
+                
+
+            dfa = pd.DataFrame(
+            data={
+                'text': ['entity extracted from your text data'], 'score': ['accuracy score; how accurately a tag has been assigned to a given entity'], 'label': ['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="zip file.zip",
+                mime="application/zip",
+                )
+        
+        
+        
+     
+                    
+st.divider()
+st.write(f"Number of times you requested results: {st.session_state['file_upload_attempts']}/{max_attempts}")