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from transformers import pipeline
import gradio as gr
# Load pipelines
sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
ner_tagger = pipeline("ner", model="dslim/bert-base-NER", grouped_entities=True)
# Translation models for different languages
translation_models = {
"French": "Helsinki-NLP/opus-mt-en-fr",
"German": "Helsinki-NLP/opus-mt-en-de",
"Spanish": "Helsinki-NLP/opus-mt-en-es",
"Hindi": "Helsinki-NLP/opus-mt-en-hi",
"Tamil": "ItchyChin/OrpoLlama-3-8B-memorize-translate-tamil-20241009",
"Japanese": "Helsinki-NLP/opus-mt-en-ja",
"Chinese": "Helsinki-NLP/opus-mt-en-zh",
"Russian": "Helsinki-NLP/opus-mt-en-ru",
"Arabic": "Helsinki-NLP/opus-mt-en-ar"
}
# Main function
def analyze_text(sentence, target_language):
# Perform Sentiment Analysis
sentiment = sentiment_analyzer(sentence)
# Perform Named Entity Recognition
ner = ner_tagger(sentence)
# Perform Translation
model_id = translation_models[target_language]
translator = pipeline("translation", model=model_id)
translated = translator(sentence, max_length=40)[0]['translation_text']
return sentiment, ner, translated
# Gradio UI
interface = gr.Interface(
fn=analyze_text,
inputs=[
gr.Textbox(label="Enter an English Sentence"),
gr.Dropdown(choices=list(translation_models.keys()), label="Translate to Language")
],
outputs=[
gr.JSON(label="Sentiment Analysis"),
gr.JSON(label="Named Entities"),
gr.Textbox(label="Translation Result")
],
title="🌍 NLP Translator + NER + Sentiment",
description="This tool analyzes a sentence for sentiment, named entities, and translates it into a chosen language using Hugging Face Transformers."
)
interface.launch()
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