app.py

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}**")