app.py

import os
import gradio as gr
import pandas as pd
import numpy as np
import json
import plotly.express as px
from huggingface_hub import InferenceClient

# Load or download spaCy English model dynamically
import spacy
try:
    nlp = spacy.load("en_core_web_sm")
except OSError:
    import spacy.cli
    spacy.cli.download("en_core_web_sm")
    nlp = spacy.load("en_core_web_sm")

def load_data(file_obj):
    """Safely loads CSV, Excel, or TXT file into a Pandas DataFrame."""
    if file_obj is None:
        return None, gr.update(choices=[], visible=False), "Please upload a file."
    
    file_path = file_obj.name
    ext = os.path.splitext(file_path)[1].lower()
    
    try:
        if ext == '.csv':
            df = pd.read_csv(file_path)
        elif ext in ['.xls', '.xlsx']:
            df = pd.read_excel(file_path)
        elif ext == '.txt':
            with open(file_path, 'r', encoding='utf-8') as f:
                content = f.read()
            df = pd.DataFrame({'text': [content]})
        else:
            return None, gr.update(choices=[], visible=False), "Unsupported file format. Please upload .csv, .xlsx, or .txt."
        
        # Find object/string columns for dropdown
        string_cols = [col for col in df.columns if df[col].dtype == 'object' or df[col].astype(str).str.len().mean() > 5]
        if not string_cols:
            string_cols = list(df.columns)
            
        return df, gr.update(choices=string_cols, value=string_cols[0], visible=True), f"Successfully loaded dataset with {len(df)} rows."
    except Exception as e:
        return None, gr.update(choices=[], visible=False), f"Error loading file: {str(e)}"

def get_highlighted_text(text, entities):
    """Helper to convert entities into Gradio's HighlightedText list-of-tuples format."""
    # entities list of dicts: {"start": int, "end": int, "label": str}
    # Sort entities by start index
    entities = sorted(entities, key=lambda x: x["start"])
    
    highlighted = []
    last_idx = 0
    for ent in entities:
        start, end, label = ent["start"], ent["end"], ent["label"]
        if start < last_idx:
            continue  # Avoid overlapping issues
        if start > last_idx:
            highlighted.append((text[last_idx:start], None))
        highlighted.append((text[start:end], label))
        last_idx = end
    if last_idx < len(text):
        highlighted.append((text[last_idx:], None))
    return highlighted

def run_spacy_ner(text):
    """Runs local SpaCy NER on a single string."""
    doc = nlp(text)
    entities = []
    for ent in doc.ents:
        entities.append({
            "text": ent.text,
            "label": ent.label_,
            "start": ent.start_char,
            "end": ent.end_char
        })
    return entities

def run_transformer_ner_api(text, hf_token, model_name):
    """Runs state-of-the-art transformer NER using student's personal HF token."""
    if not hf_token:
        raise ValueError("Hugging Face API Token is required for Transformer Mode.")
        
    client = InferenceClient(token=hf_token)
    
    # We use HF Token Classification API
    try:
        # returns list of dicts: [{'entity_group': 'PER', 'score': 0.99, 'word': '...', 'start': 0, 'end': 5}]
        response = client.token_classification(text, model=model_name)
    except Exception as e:
        raise RuntimeError(f"Hugging Face Inference API error: {str(e)}")
        
    entities = []
    for item in response:
        # Standardize labels from CONLL/standard formats
        label = item.get("entity_group", item.get("entity", "ENTITY"))
        if label.startswith("B-") or label.startswith("I-"):
            label = label[2:]  # Strip BIO prefixes for clean visualization
            
        entities.append({
            "text": item.get("word", ""),
            "label": label,
            "start": item.get("start", 0),
            "end": item.get("end", 0)
        })
    return entities

def analyze_ner(text_input, file_obj, text_col, method, hf_token, hf_model):
    # Determine the input documents
    docs = []
    if file_obj is not None:
        df, _, _ = load_data(file_obj)
        if df is not None and text_col in df.columns:
            docs = df[text_col].astype(str).fillna("").tolist()
    elif text_input and text_input.strip():
        docs = [text_input]
        
    if not docs:
        return None, None, None, None, "Please enter text or upload a valid dataset first."
        
    all_extracted = []
    
    # Process documents
    for doc_idx, doc_text in enumerate(docs):
        try:
            if method == "spaCy (Local & Fast)":
                ents = run_spacy_ner(doc_text)
            else:
                ents = run_transformer_ner_api(doc_text, hf_token, hf_model)
                
            for e in ents:
                all_extracted.append({
                    "Doc_Index": doc_idx + 1,
                    "Entity_Text": e["text"],
                    "Label": e["label"],
                    "Start_Char": e["start"],
                    "End_Char": e["end"],
                    "Context": f"...{doc_text[max(0, e['start']-30):min(len(doc_text), e['end']+30)]}..."
                })
        except Exception as e:
            return None, None, None, None, f"Error processing row {doc_idx + 1}: {str(e)}"
            
    if not all_extracted:
        return (
            [("No entities found in the text.", None)],
            pd.DataFrame(),
            None, None, "Analysis finished: No named entities were detected."
        )
        
    df_ents = pd.DataFrame(all_extracted)
    
    # 1. Visualization format for the first document (to show beautiful color-highlighted text in UI)
    first_doc_text = docs[0]
    first_doc_ents = [e for e in all_extracted if e["Doc_Index"] == 1]
    # Standardize keys
    highlight_ents = [{"start": e["Start_Char"], "end": e["End_Char"], "label": e["Label"]} for e in first_doc_ents]
    highlighted_output = get_highlighted_text(first_doc_text, highlight_ents)
    
    # 2. Statistics Bar Chart
    label_counts = df_ents["Label"].value_counts().reset_index()
    label_counts.columns = ["Entity Type", "Count"]
    fig = px.bar(
        label_counts, 
        x="Entity Type", 
        y="Count", 
        color="Entity Type",
        title="Distribution of Extracted Entity Types",
        template="plotly_dark"
    )
    fig.update_layout(height=350, margin=dict(l=20, r=20, t=40, b=20))
    
    # 3. Save export files
    csv_path = "extracted_entities.csv"
    json_path = "extracted_entities.json"
    
    df_ents.to_csv(csv_path, index=False)
    
    # Save formatted JSON
    with open(json_path, 'w', encoding='utf-8') as f:
        json.dump(all_extracted, f, indent=4, ensure_ascii=False)
        
    # Clean table for UI display
    df_table = df_ents[["Doc_Index", "Entity_Text", "Label", "Context"]].copy()
    
    return highlighted_output, df_table, fig, csv_path, json_path

custom_css = """
body {
    background-color: #0b0f19;
    color: #f3f4f6;
}
.gradio-container {
    font-family: 'Inter', sans-serif !important;
}
h1, h2 {
    color: #6366f1 !important;
}
"""

with gr.Blocks(theme=gr.themes.Default(primary_hue="indigo", secondary_hue="slate"), css=custom_css) as demo:
    df_state = gr.State()
    
    gr.HTML("""
    <div style="text-align: center; margin-bottom: 2rem;">
        <h1 style="font-size: 2.5rem; font-weight: 700; margin-bottom: 0.5rem; background: linear-gradient(to right, #6366f1, #a855f7); -webkit-background-clip: text; -webkit-text-fill-color: transparent;">Interactive Named Entity Recognizer</h1>
        <p style="font-size: 1.1rem; color: #94a3b8; max-width: 800px; margin: 0 auto;">
            Extract and analyze people, places, dates, and organizations from raw text or datasets.
            Runs locally on standard models, or unlocks state-of-the-art Transformer models using your personal Hugging Face Token.
        </p>
    </div>
    """)
    
    with gr.Row():
        # Left Panel: Input controls
        with gr.Column(scale=1):
            gr.Markdown("### 1. Choose Input Source")
            with gr.Tabs():
                with gr.TabItem("Paste Raw Text"):
                    text_input = gr.Textbox(
                        label="Source Text",
                        placeholder="Paste your text here (e.g., 'Apple Inc. was founded by Steve Jobs in Cupertino, California...').",
                        lines=10
                    )
                with gr.TabItem("Upload Dataset File"):
                    file_input = gr.File(label="Upload (.csv, .xlsx, .txt)", file_types=[".csv", ".xlsx", ".txt"])
                    text_column_selector = gr.Dropdown(
                        label="Target Text Column", 
                        choices=[], 
                        visible=False,
                        interactive=True
                    )
                    status_text = gr.Markdown("No file uploaded yet.")
                    
            gr.Markdown("### 2. Configure Model")
            method_selector = gr.Radio(
                choices=["spaCy (Local & Fast)", "Transformers (API Mode)"],
                value="spaCy (Local & Fast)",
                label="Extraction Model"
            )
            
            with gr.Group() as token_group:
                hf_token_input = gr.Textbox(
                    label="Hugging Face API Token",
                    placeholder="hf_...",
                    type="password",
                    visible=False,
                    info="Required to call advanced transformer models. Get one free at huggingface.co."
                )
                hf_model_input = gr.Dropdown(
                    choices=[
                        "dbmdz/bert-large-cased-finetuned-conll03-english",
                        "dslim/bert-base-NER",
                        "Babelscape/wikineural-multilingual-ner"
                    ],
                    value="dbmdz/bert-large-cased-finetuned-conll03-english",
                    label="Transformer Model (HF API)",
                    visible=False
                )
                
            run_btn = gr.Button("Extract Entities", variant="primary")
            
        # Right Panel: Results
        with gr.Column(scale=2):
            gr.Markdown("### 3. Extracted Named Entities")
            
            with gr.Tabs():
                with gr.TabItem("Visual Color-Highlighting"):
                    highlighted_output = gr.HighlightedText(
                        label="First Document Entity Highlight",
                        combine_adjacent=False
                    )
                with gr.TabItem("Full Analysis Table"):
                    table_output = gr.Dataframe(
                        headers=["Doc_Index", "Entity_Text", "Label", "Context"],
                        datatype=["number", "str", "str", "str"],
                        interactive=False,
                        wrap=True
                    )
                with gr.TabItem("Statistics Chart"):
                    chart_output = gr.Plot(label="Entity Frequency Plot")
                    
            gr.Markdown("### 4. Export & Download")
            with gr.Row():
                download_csv = gr.File(label="Download CSV Report")
                download_json = gr.File(label="Download JSON Report")

    # Show/hide token field depending on model
    def toggle_method_fields(method):
        if method == "Transformers (API Mode)":
            return gr.update(visible=True), gr.update(visible=True)
        else:
            return gr.update(visible=False), gr.update(visible=False)
            
    method_selector.change(
        fn=toggle_method_fields,
        inputs=method_selector,
        outputs=[hf_token_input, hf_model_input]
    )
    
    file_input.change(
        fn=load_data,
        inputs=file_input,
        outputs=[df_state, text_column_selector, status_text]
    )
    
    run_btn.click(
        fn=analyze_ner,
        inputs=[text_input, file_input, text_column_selector, method_selector, hf_token_input, hf_model_input],
        outputs=[highlighted_output, table_output, chart_output, download_csv, download_json]
    )

if __name__ == "__main__":
    demo.launch()