Create app.py

app.py

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+import time
+import streamlit as st
+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
+import os
+from comet_ml import Experiment
+import re
+import numpy as np
+import json
+from cryptography.fernet import Fernet 
+
+st.set_page_config(layout="wide", page_title="Named Entity Recognition App")
+
+
+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
+
+
+if 'file_upload_attempts' not in st.session_state:
+    st.session_state['file_upload_attempts'] = 0
+
+
+if 'encrypted_extracted_text' not in st.session_state:
+    st.session_state['encrypted_extracted_text'] = None
+
+
+if 'json_dataframe' not in st.session_state:
+    st.session_state['json_dataframe'] = None
+
+max_attempts = 10 
+
+
+@st.cache_resource
+def load_ner_model():
+    
+    try:
+        return pipeline("token-classification",
+                        model="saattrupdan/nbailab-base-ner-scandi",
+                        aggregation_strategy="max",
+                        stride=128)
+    except Exception as e:
+        st.error(f"Failed to load NER model. Please check your internet connection or model availability: {e}")
+        st.stop()
+
+
+
+@st.cache_resource
+def load_encryption_key():
+    """
+    Loads the Fernet encryption key from environment variables.
+    This key is crucial for encrypting/decrypting sensitive data.
+    It's cached as a resource to be loaded only once.
+    """
+    try:
+        # Get the key string from environment variables
+        key_str = os.environ.get("FERNET_KEY")
+        if not key_str:
+            raise ValueError("FERNET_KEY environment variable not set. Cannot perform encryption/decryption.")
+        
+        # Fernet key must be bytes, so encode the string
+        key_bytes = key_str.encode('utf-8')
+        return Fernet(key_bytes)
+    except ValueError as ve:
+        st.error(f"Configuration Error: {ve}. Please ensure the 'FERNET_KEY' environment variable is set securely in your deployment environment (e.g., Hugging Face Spaces secrets, Render environment variables) or in a local .env file for development.")
+        st.stop() # Stop the app if the key is not found, as security is compromised
+    except Exception as e:
+        st.error(f"An unexpected error occurred while loading encryption key: {e}. Please check your key format and environment settings.")
+        st.stop()
+
+# Initialize the Fernet cipher instance
+fernet = load_encryption_key()
+
+def encrypt_text(text_content: str) -> bytes:
+    """
+    Encrypts a string using the loaded Fernet cipher.
+    The input string is first encoded to UTF-8 bytes.
+    """
+    return fernet.encrypt(text_content.encode('utf-8'))
+
+def decrypt_text(encrypted_bytes: bytes) -> str | None:
+    """
+    Decrypts bytes using the loaded Fernet cipher.
+    Returns the decrypted string, or None if decryption fails (e.g., tampering).
+    """
+    try:
+        return fernet.decrypt(encrypted_bytes).decode('utf-8')
+    except Exception as e:
+        st.error(f"Decryption failed. This might indicate data tampering or an incorrect encryption key. Error: {e}")
+        return None
+
+# --- UI Elements ---
+st.subheader("Scandinavian JSON Entity Finder", divider="orange")
+st.link_button("by nlpblogs", "https://nlpblogs.com", type="tertiary")
+
+expander = st.expander("**Important notes on the Scandinavian JSON Entity Finder**")
+expander.write('''
+   **Named Entities:** This Scandinavian JSON Entity Finder predicts four
+   (4) labels (“PER: person”, “LOC: location”, “ORG: organization”, “MISC:
+   miscellaneous”). 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 JSON 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.
+   
+   **Language settings:** Please check and adjust the language settings in
+   your computer, so the Danish, Swedish, Norwegian, Icelandic and Faroese
+   characters are handled properly in your downloaded file.
+   
+   **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="orange")
+    st.link_button("Italian URL & TXT Entity Finder",
+                   "https://nlpblogs.com/shop/named-entity-recognition-ner/monolingual-ner-web-apps/italian-url-txt-entity-finder/",
+                   type="primary")
+
+
+uploaded_file = st.file_uploader("Choose a JSON file", type=["json"])
+
+# Initialize text for the current run outside the if uploaded_file block
+# This will be populated if a file is uploaded, otherwise it remains None
+current_run_text = None
+
+if uploaded_file is not None:
+    try:
+        # Read the content as bytes first, then decode for JSON parsing
+        file_contents_bytes = uploaded_file.read()
+        
+        # Reset the file pointer after reading, so json.load can read from the beginning
+        uploaded_file.seek(0)
+        dados = json.load(uploaded_file)
+
+        # Attempt to convert JSON to DataFrame and extract text
+        try:
+            st.session_state['json_dataframe'] = pd.DataFrame(dados)
+            
+            # Concatenate all content into a single string for NER
+            df_string_representation = st.session_state['json_dataframe'].to_string(index=False, header=False)
+            # Simple regex to remove non-alphanumeric characters but keep spaces and periods
+            text_content = re.sub(r'[^\w\s.]', '', df_string_representation)
+            # Remove the specific string "Empty DataFrame Columns" if it appears due to conversion
+            text_content = text_content.replace("Empty DataFrame Columns", "").strip()
+            current_run_text = text_content # Set text for current run
+
+            if not current_run_text.strip(): # Check if text is effectively empty
+                st.warning("No meaningful text could be extracted from the JSON DataFrame for analysis.")
+                current_run_text = None # Reset to None if empty
+
+        except ValueError:
+            # If direct conversion to DataFrame fails, try to extract strings directly from JSON structure
+            st.info("JSON data could not be directly converted to a simple DataFrame for display. Attempting to extract text directly.")
+            extracted_texts_list = []
+            if isinstance(dados, list):
+                for item in dados:
+                    if isinstance(item, str):
+                        extracted_texts_list.append(item)
+                    elif isinstance(item, dict):
+                        # Recursively get string values from dicts in a list
+                        for val in item.values():
+                            if isinstance(val, str):
+                                extracted_texts_list.append(val)
+                            elif isinstance(val, list):
+                                for sub_val in val:
+                                    if isinstance(sub_val, str):
+                                        extracted_texts_list.append(sub_val)
+            elif isinstance(dados, dict):
+                # Get string values from a dictionary
+                for value in dados.values():
+                    if isinstance(value, str):
+                        extracted_texts_list.append(value)
+                    elif isinstance(value, list):
+                        for sub_val in value:
+                            if isinstance(sub_val, str):
+                                extracted_texts_list.append(sub_val)
+
+            if extracted_texts_list:
+                current_run_text = " ".join(extracted_texts_list).strip()
+            else:
+                st.warning("No string text could be extracted from the JSON for analysis.")
+                current_run_text = None
+
+        if current_run_text:
+            # --- ENCRYPT THE EXTRACTED TEXT BEFORE STORING IN SESSION STATE ---
+            encrypted_text_bytes = encrypt_text(current_run_text)
+            st.session_state['encrypted_extracted_text'] = encrypted_text_bytes
+            # Optionally clear the unencrypted version from session state if you only want the encrypted one
+            # st.session_state['extracted_text_for_ner'] = None 
+            
+            st.success("JSON file uploaded successfully. File content encrypted and secured. Due to security protocols, the file content is hidden.")
+            st.divider()
+        else:
+            st.session_state['encrypted_extracted_text'] = None
+            # st.session_state['extracted_text_for_ner'] = None
+            st.error("Could not extract meaningful text from the uploaded JSON file.")
+
+    except json.JSONDecodeError as e:
+        st.error(f"JSON Decode Error: {e}")
+        st.error("Please ensure the uploaded file contains valid JSON data.")
+        st.session_state['encrypted_extracted_text'] = None
+        st.session_state['json_dataframe'] = None
+    except Exception as e:
+        st.error(f"An unexpected error occurred during file processing: {e}")
+        st.session_state['encrypted_extracted_text'] = None
+        st.session_state['json_dataframe'] = None
+
+
+# --- Results Button and Processing Logic ---
+if st.button("Results"):
+    start_time = time.time()
+    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()
+
+    # --- DECRYPT THE TEXT BEFORE PASSING TO NER MODEL ---
+    text_for_ner = None
+    if st.session_state['encrypted_extracted_text'] is not None:
+        text_for_ner = decrypt_text(st.session_state['encrypted_extracted_text'])
+    
+    if text_for_ner is None or not text_for_ner.strip():
+        st.warning("No extractable text content available for analysis. Please upload a valid JSON file.")
+        st.stop()
+
+    st.session_state['file_upload_attempts'] += 1
+
+    with st.spinner("Analyzing text...", show_time=True):
+        model = load_ner_model()
+        text_entities = model(text_for_ner) # Use the decrypted text
+        df = pd.DataFrame(text_entities)
+
+        if 'word' in df.columns:
+            # Ensure 'word' column is string type before applying regex
+            if df['word'].dtype == 'object': 
+                pattern = r'[^\w\s]' # Regex to remove non-alphanumeric characters but keep spaces and periods
+                df['word'] = df['word'].astype(str).replace(pattern, '', regex=True)
+            else:
+                st.warning("The 'word' column is not of string type; skipping character cleaning.")
+        else:
+            st.error("The 'word' column does not exist in the DataFrame. Cannot perform cleaning.")
+            st.stop() # Stop execution if the column is missing
+
+        # Replace empty strings with 'Unknown' and drop rows with NaN after cleaning
+        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_for_ner))
+            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']
+            ''')
+
+        entity_groups = {"PER": "person",
+                         "LOC": "location",
+                         "ORG": "organization",
+                         "MISC": "miscellaneous",
+                        }
+
+        st.subheader("Grouped entities", divider = "orange")
+
+        # Convert entity_groups dictionary to a list of (key, title) tuples
+        entity_items = list(entity_groups.items())
+        # Define how many tabs per row
+        tabs_per_row = 5
+        # Loop through the entity items in chunks
+        for i in range(0, len(entity_items), tabs_per_row):
+            current_row_entities = entity_items[i : i + tabs_per_row]
+            tab_titles = [item[1] for item in current_row_entities]
+
+            tabs = st.tabs(tab_titles)
+            for j, (entity_group_key, tab_title) in enumerate(current_row_entities):
+                with tabs[j]:
+                    if entity_group_key in df["entity_group"].unique():
+                        df_filtered = df[df["entity_group"] == entity_group_key]
+                        st.dataframe(df_filtered, use_container_width=True)
+                    else:
+                        st.info(f"No '{tab_title}' entities found in the text.")
+                        # Display an empty DataFrame for consistency if no entities are found
+                        st.dataframe(pd.DataFrame({
+                            'entity_group': [entity_group_key],
+                            'score': [np.nan],
+                            'word': [np.nan],
+                            'start': [np.nan],
+                            'end': [np.nan]
+                        }), hide_index=True)
+
+        st.divider()
+
+        # --- Visualizations ---
+        st.subheader("Tree map", divider="orange")
+        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="orange")
+            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="orange")
+            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={
+                'Column Name': ['word', 'entity_group','score', 'start', 'end'],
+                'Description': [
+                    'entity extracted from your text data',
+                    'label (tag) assigned to a given extracted entity',
+                    'accuracy score; how accurately a tag has been assigned to a given entity',
+                    'index of the start of the corresponding entity',
+                    '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()
+    end_time = time.time()
+    elapsed_time = end_time - start_time
+    st.info(f"Results processed in **{elapsed_time:.2f} seconds**.")
+st.write(f"Number of times you requested results: **{st.session_state['file_upload_attempts']}/{max_attempts}**")