app.py

import gradio as gr
from transformers import DistilBertForTokenClassification, DistilBertTokenizerFast
import torch

# Load Model & Tokenizer
model_name = "AventIQ-AI/distilbert-base-uncased_token_classification"
model = DistilBertForTokenClassification.from_pretrained(model_name)
tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)

# Define Icon Mapping for Entities
ICON_MAP = {
    "Corporation": "🏢",
    "Person": "👤",
    "Product": "📱",
    "Location": "📍",
    "Creative-Work": "🎭",
    "Group": "👥"
}

def predict_entities(text):
    """Predict Named Entities using the model."""
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    
    with torch.no_grad():
        outputs = model(**inputs)
        predictions = torch.argmax(outputs.logits.float(), dim=2)  # Convert logits to float32
    
    predicted_labels = [model.config.id2label[t.item()] for t in predictions[0]]
    tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])

    # Process Entities
    entities = []
    current_entity = None

    for token, label in zip(tokens, predicted_labels):
        if token in [tokenizer.cls_token, tokenizer.sep_token, tokenizer.pad_token]:
            continue
            
        if token.startswith("##"):
            if current_entity:
                current_entity["text"] += token[2:]
            continue
            
        if label == "O":
            if current_entity:
                entities.append(current_entity)
                current_entity = None
        else:
            if label.startswith("B-"):
                if current_entity:
                    entities.append(current_entity)
                current_entity = {"text": token, "type": label[2:]}
            elif label.startswith("I-") and current_entity:
                current_entity["text"] += " " + token

    if current_entity:
        entities.append(current_entity)

    return format_output(text, entities)

def format_output(text, entities):
    """Format output for Gradio UI."""
    output = f"📥 **Input**: {text}\n\n🔍 **Detected Entities**:\n"
    
    if not entities:
        output += "ℹ️ No named entities detected. Try another sentence!\n"
    else:
        for entity in entities:
            icon = ICON_MAP.get(entity["type"], "🔹")
            output += f"- {icon} **{entity['text']}** → `{entity['type']}`\n"
    
    return output

# Create Gradio UI
gr.Interface(
    fn=predict_entities,
    inputs=gr.Textbox(placeholder="Enter text here...", label="Input Text"),
    outputs=gr.Textbox(label="NER Output"),
    title="📝 Named Entity Recognition (NER)",
    description="🔍 Enter a sentence and the model will detect entities like **Person, Location, Product, etc.**",
    theme="default",
    allow_flagging="never"
).launch()