src/streamlit_app.py

import os
os.environ['HF_HOME'] = '/tmp'
import time
import streamlit as st
import pandas as pd
import io
import plotly.express as px
import zipfile
import json
from cryptography.fernet import Fernet
from streamlit_extras.stylable_container import stylable_container
from typing import Optional
from gliner import GLiNER
from comet_ml import Experiment

st.markdown(
    """
    <style>
    /* Overall app container */
    .stApp {
        background-color: #F5F5F5; /* A very light grey */
        color: #333333; /* Dark grey for text for good contrast */
    }
    /* Sidebar background */
    .css-1d36184, .css-1d36184, .st-ck {
        background-color: #D3D3D3; /* Light grey for the sidebar */
    }
    /* Expander header and content background */
    .streamlit-expanderHeader, .streamlit-expanderContent {
        background-color: #F5F5F5;
    }
    /* Text Area background and text color */
    .stTextArea textarea {
        background-color: #E6E6E6; /* Slightly darker grey for input fields */
        color: #000000;
        border: 1px solid #B0B0B0; /* Add a subtle border */
    }
    /* Button styling */
    .stButton > button {
        background-color: #B0B0B0; /* A medium grey for the button */
        color: #FFFFFF; /* White text for contrast */
        border: none;
        padding: 10px 20px;
        border-radius: 5px;
    }
    .stButton > button:hover {
        background-color: #8C8C8C; /* Darker grey on hover */
    }
    /* Alert boxes */
    .stAlert {
        color: #000000;
        border-left: 5px solid #8C8C8C; /* A dark grey border for a clean look */
    }
    .stAlert.st-warning {
        background-color: #C0C0C0; /* Silver grey for warning */
    }
    .stAlert.st-success {
        background-color: #C0C0C0; /* Silver grey for success */
    }
    </style>
    """,
    unsafe_allow_html=True
)

# --- Page Configuration and UI Elements ---
st.set_page_config(layout="wide", page_title="Named Entity Recognition App")
st.subheader("MediExtract", divider="gray")
st.link_button("by nlpblogs", "https://nlpblogs.com", type="tertiary")
expander = st.expander("**Important notes**")
expander.write("""**Named Entities:** This MediExtract web app predicts sixteen (16) labels: "Disease", "Symptom", "Medication", "Dosage", "Frequency", "Procedure", "Diagnostic_test", "Lab_value", "Gene", "Protein", "Anatomy", "Cell_type", "Chemical", "Person", "Organization", "Date"

Results are presented in easy-to-read tables, 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:** Type or paste your text into the text area below, then press Ctrl + Enter. Click the 'Results' button to extract and tag entities in your text data.

**Usage Limits:** You can request results unlimited times for one (1) month.

**Supported Languages:** English

**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:
    st.write("Use the following code to embed the MediExtract web app on your website. Feel free to adjust the width and height values to fit your page.")
    code = '''
    <iframe
	src="https://aiecosystem-mediextract.hf.space"
	frameborder="0"
	width="850"
	height="450"
    ></iframe>

    '''
    st.code(code, language="html")
    st.text("")
    st.text("")
    st.divider()
    st.subheader("🚀 Ready to build your own AI Web App?", divider="gray")
    st.link_button("AI Web App Builder", "https://nlpblogs.com/build-your-named-entity-recognition-app/", type="primary")

# --- Comet ML Setup ---
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 = bool(COMET_API_KEY and COMET_WORKSPACE and COMET_PROJECT_NAME)
if not comet_initialized:
    st.warning("Comet ML not initialized. Check environment variables.")

# --- Label Definitions ---
labels = [
    "Disease",
    "Symptom",
    "Medication",
    "Dosage",
    "Frequency",
    "Procedure",
    "Diagnostic_test",
    "Lab_value",
    "Gene",
    "Protein",
    "Anatomy",
    "Cell_type",
    "Chemical",
    "Person",
    "Organization",
    "Date"
]
# Create a mapping dictionary for labels to categories
category_mapping = {
    "Clinical & Procedural": [
        "Disease",
        "Symptom",
        "Procedure"
    ],
    "Medication & Treatment": [
        "Medication",
        "Dosage",
        "Frequency"
    ],
    "Measurements & Results": [
        "Diagnostic_test",
        "Lab_value"
    ],
    "Biological & Anatomical": [
        "Gene",
        "Protein",
        "Anatomy",
        "Cell_type",
        "Chemical"
    ],
    "People & Groups": [
        "Person",
        "Organization"
    ],
    "Temporal": [
        "Date"
    ]
}

# --- Model Loading ---
@st.cache_resource
def load_ner_model():
    """Loads the GLiNER model and caches it."""
    try:
        return GLiNER.from_pretrained("Ihor/gliner-biomed-bi-large-v1.0", nested_ner=True, num_gen_sequences=2, gen_constraints=labels)
    except Exception as e:
        st.error(f"Failed to load NER model. Please check your internet connection or model availability: {e}")
        st.stop()

model = load_ner_model()
# Flatten the mapping to a single dictionary
reverse_category_mapping = {label: category for category, label_list in category_mapping.items() for label in label_list}

# --- Session State Initialization ---
# This is the key fix. We use session state to control what is displayed.
if 'show_results' not in st.session_state:
    st.session_state.show_results = False
if 'last_text' not in st.session_state:
    st.session_state.last_text = ""
if 'results_df' not in st.session_state:
    st.session_state.results_df = pd.DataFrame()
if 'elapsed_time' not in st.session_state:
    st.session_state.elapsed_time = 0.0

# --- Text Input and Clear Button ---
word_limit = 200
text = st.text_area(f"Type or paste your text below (max {word_limit} words), and then press Ctrl + Enter", height=250, key='my_text_area')
word_count = len(text.split())
st.markdown(f"**Word count:** {word_count}/{word_limit}")

def clear_text():
    """Clears the text area and hides results."""
    st.session_state['my_text_area'] = ""
    st.session_state.show_results = False
    st.session_state.last_text = ""
    st.session_state.results_df = pd.DataFrame()
    st.session_state.elapsed_time = 0.0

st.button("Clear text", on_click=clear_text)

# --- Results Section ---
if st.button("Results"):
    if not text.strip():
        st.warning("Please enter some text to extract entities.")
        st.session_state.show_results = False
    elif word_count > word_limit:
        st.warning(f"Your text exceeds the {word_limit} word limit. Please shorten it to continue.")
        st.session_state.show_results = False
    else:
        # Check if the text is different from the last time
        if text != st.session_state.last_text:
            st.session_state.show_results = True
            st.session_state.last_text = text
            start_time = time.time()
            with st.spinner("Extracting entities...", show_time=True):
                entities = model.predict_entities(text, labels)
                df = pd.DataFrame(entities)
                st.session_state.results_df = df
                if not df.empty:
                    df['category'] = df['label'].map(reverse_category_mapping)
                    if comet_initialized:
                        experiment = Experiment(
                            api_key=COMET_API_KEY,
                            workspace=COMET_WORKSPACE,
                            project_name=COMET_PROJECT_NAME,
                        )
                        experiment.log_parameter("input_text", text)
                        experiment.log_table("predicted_entities", df)
                        experiment.end()
            end_time = time.time()
            st.session_state.elapsed_time = end_time - start_time
        else:
            # If the text is the same, just show the cached results without re-running
            st.session_state.show_results = True

# Display results if the state variable is True
if st.session_state.show_results:
    df = st.session_state.results_df
    if not df.empty:
        df['category'] = df['label'].map(reverse_category_mapping)
        st.subheader("Grouped Entities by Category", divider="gray")

        # Create tabs for each category
        category_names = sorted(list(category_mapping.keys()))
        category_tabs = st.tabs(category_names)

        for i, category_name in enumerate(category_names):
            with category_tabs[i]:
                df_category_filtered = df[df['category'] == category_name]
                if not df_category_filtered.empty:
                    st.dataframe(df_category_filtered.drop(columns=['category']), use_container_width=True)
                else:
                    st.info(f"No entities found for the '{category_name}' category.")
        
        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']
            ''')
        st.divider()
        
        # Tree map
        st.subheader("Tree map", divider="gray")
        fig_treemap = px.treemap(df, path=[px.Constant("all"), 'category', 'label', 'text'], values='score', color='category')
        fig_treemap.update_layout(margin=dict(t=50, l=25, r=25, b=25), paper_bgcolor='#F5F5F5', plot_bgcolor='#F5F5F5')
        st.plotly_chart(fig_treemap)
        
        # Pie and Bar charts
        grouped_counts = df['category'].value_counts().reset_index()
        grouped_counts.columns = ['category', 'count']
        col1, col2 = st.columns(2)
        
        with col1:
            st.subheader("Pie chart", divider="gray")
            fig_pie = px.pie(grouped_counts, values='count', names='category', hover_data=['count'], labels={'count': 'count'}, title='Percentage of predicted categories')
            fig_pie.update_traces(textposition='inside', textinfo='percent+label')
            fig_pie.update_layout(
                paper_bgcolor='#F5F5F5',
                plot_bgcolor='#F5F5F5'
            )
            st.plotly_chart(fig_pie)
            
        with col2:
            st.subheader("Bar chart", divider="gray")
            fig_bar = px.bar(grouped_counts, x="count", y="category", color="category", text_auto=True, title='Occurrences of predicted categories')
            fig_bar.update_layout(
                paper_bgcolor='#F5F5F5',
                plot_bgcolor='#F5F5F5'
            )
            st.plotly_chart(fig_bar)
            
        # Most Frequent Entities
        st.subheader("Most Frequent Entities", divider="gray")
        word_counts = df['text'].value_counts().reset_index()
        word_counts.columns = ['Entity', 'Count']
        repeating_entities = word_counts[word_counts['Count'] > 1]
        
        if not repeating_entities.empty:
            st.dataframe(repeating_entities, use_container_width=True)
            fig_repeating_bar = px.bar(repeating_entities, x='Entity', y='Count', color='Entity')
            fig_repeating_bar.update_layout(xaxis={'categoryorder': 'total descending'},
                                            paper_bgcolor='#F5F5F5',
                                            plot_bgcolor='#F5F5F5')
            st.plotly_chart(fig_repeating_bar)
        else:
            st.warning("No entities were found that occur more than once.")
        
        # Download Section
        st.divider()
        dfa = pd.DataFrame(
            data={
                'Column Name': ['text', 'label', '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: #8C8C8C; border: 1px solid black; padding: 5px; color: white; }""",
        ):
            st.download_button(
                label="Download results and glossary (zip)",
                data=buf.getvalue(),
                file_name="nlpblogs_results.zip",
                mime="application/zip",
            )
        
        st.text("")
        st.text("")
        st.info(f"Results processed in **{st.session_state.elapsed_time:.2f} seconds**.")
    
    else:  # If df is empty after the button click
        st.warning("No entities were found in the provided text.")