Delete app.py

app.py

@@ -1,233 +0,0 @@
-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
-
-# --- Page Configuration and UI Elements ---
-st.set_page_config(layout="wide", page_title="Named Entity Recognition App")
-
-st.subheader("ProductTag", divider="orange")
-st.link_button("by nlpblogs", "https://nlpblogs.com", type="tertiary")
-
-expander = st.expander("**Important notes on the ProductTag**")
-expander.write("""
-   **Named Entities:** This ProductTag predicts twenty-four (24) labels: "Product", "Service", "Organization", "Company", "Currency", "City", "Country", "Region", "Market", "Store", "Shop", "Customer_segment", "Demographics", "Target_market", "Market_segment", "Fiscal_period", "Timeframe", "Date", "Campaign", "Advertisement", "Event", "Media_platform", "Media_channel", "Social_media_platform"
-   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) week.
-   **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 [email protected]
-""")
-
-with st.sidebar:
-    st.subheader("Build your own NER Web App in a minute without writing a single line of code.", divider="orange")
-    st.link_button("NER File Builder", "https://nlpblogs.com/shop/named-entity-recognition-ner/ner-file-builder/", type="primary")
-
-    st.text("")
-    st.text("")
-
-    st.write("Use the following code to embed the ProductTag web app on your website. Feel free to adjust the width and height values to fit your page.")
-    code = '''
-    <iframe
-    src="https://aiecosystem-producttag.hf.space"
-    frameborder="0"
-    width="850"
-    height="450"
-    ></iframe>
-    '''
-    st.code(code, language="html")
-
-# --- 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.")
-
-# --- Model Loading ---
-@st.cache_resource
-def load_ner_model():
-    """Loads the GLiNER model and caches it."""
-    try:
-        return GLiNER.from_pretrained("knowledgator/gliner-multitask-large-v0.5", nested_ner=True)
-    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()
-
-# --- Label Definitions ---
-labels = [
-    
-    "Product", "Service", "Organization", "Company", 
-    "Currency",
-    "City", "Country", "Region", "Market", "Store", "Shop",
-    "Customer_segment", "Demographics", "Target_market", "Market_segment",
-    "Fiscal_period", "Timeframe", "Date",
-    "Campaign", "Advertisement", "Event",
-    "Media_platform", "Media_channel", "Social_media_platform"
-]
-
-# Create a mapping dictionary for labels to categories
-category_mapping = {
-    
-    "Product & Service Details ": ["Product", "Service", "Organization", "Company"],
-    "Financial Details" :["Currency"],
-    "Location & Geographic Information": ["City", "Country", "Region", "Market", "Store", "Shop"],
-    "Customer & Market Segments ": ["Customer_segment", "Demographics", "Target_market", "Market_segment"],
-    "Time-Based Information": ["Fiscal_period", "Timeframe", "Date"],
-    "Marketing & Campaign Details": ["Campaign", "Advertisement", "Event"],
-    "Digital & Media Information": ["Media_platform", "Media_channel", "Social_media_platform"]
-}
-# 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}
-
-# --- Text Input and Clear Button ---
-text = st.text_area("Type or paste your text below, and then press Ctrl + Enter", height=250, key='my_text_area')
-
-def clear_text():
-    """Clears the text area."""
-    st.session_state['my_text_area'] = ""
-
-st.button("Clear text", on_click=clear_text)
-st.divider()
-
-# --- Results Section ---
-if st.button("Results"):
-    start_time = time.time()
-    if not text.strip():
-        st.warning("Please enter some text to extract entities.")
-    else:
-        with st.spinner("Extracting entities...", show_time=True):
-            entities = model.predict_entities(text, labels)
-            df = pd.DataFrame(entities)
-
-            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)
-
-                st.subheader("Extracted Entities", divider = "orange")
-                st.dataframe(df.style.set_properties(**{"border": "2px solid gray", "color": "blue", "font-size": "16px"}))
-
-                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']
-                    - **category**: ['the high-level category for the label']
-                    - **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 = "orange")
-                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))
-                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 = "orange")
-                    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')
-                    st.plotly_chart(fig_pie)
-
-                with col2:
-                    st.subheader("Bar chart", divider = "orange")
-                    fig_bar = px.bar(grouped_counts, x="count", y="category", color="category", text_auto=True,
-                                    title='Occurrences of predicted categories')
-                    st.plotly_chart(fig_bar)
-
-                # Most Frequent Entities
-                st.subheader("Most Frequent Entities", divider="orange")
-                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'})
-                    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', 'category'],
-                        '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',
-                            'the broader category the entity belongs to',
-                        ]
-                    }
-                )
-
-                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 results and glossary (zip)",
-                        data=buf.getvalue(),
-                        file_name="markettag_results.zip",
-                        mime="application/zip",
-                    )
-                
-                if comet_initialized:
-                    experiment.log_figure(figure=fig_treemap, figure_name="entity_treemap_categories")
-                    experiment.end()
-
-            else: # If df is empty
-                st.warning("No entities were found in the provided text.")
-
-    end_time = time.time()
-    elapsed_time = end_time - start_time
-
-    st.text("")
-    st.text("")
-    st.info(f"Results processed in **{elapsed_time:.2f} seconds**.")