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Browse files- app.py +47 -0
- requirements.txt +2 -0
app.py
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from transformers import pipeline
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import gradio as gr
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# Load sentiment and NER pipelines
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sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
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ner_tagger = pipeline("ner", model="dslim/bert-base-NER", aggregation_strategy="simple")
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# Supported translation models
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translation_models = {
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"Hindi": "Helsinki-NLP/opus-mt-en-hi",
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"French": "Helsinki-NLP/opus-mt-en-fr"
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}
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# Load translation pipelines
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translation_pipelines = {
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lang: pipeline("translation", model=model_id)
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for lang, model_id in translation_models.items()
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}
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def nlp_assistant(sentence, language):
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sentiment_result = sentiment_analyzer(sentence)[0]
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sentiment = f"{sentiment_result['label']} (Confidence: {sentiment_result['score']:.2f})"
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ner_result = ner_tagger(sentence)
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named_entities = "\n".join([f"{ent['word']} ({ent['entity_group']})" for ent in ner_result]) if ner_result else "No named entities found."
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translator = translation_pipelines[language]
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translation = translator(sentence)[0]['translation_text']
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return sentiment, named_entities, translation
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iface = gr.Interface(
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fn=nlp_assistant,
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inputs=[
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gr.Textbox(lines=2, label="Enter an English sentence"),
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gr.Dropdown(choices=list(translation_models.keys()), label="Choose Translation Language")
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],
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outputs=[
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gr.Textbox(label="π§ Sentiment Analysis"),
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gr.Textbox(label="π Named Entity Recognition"),
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gr.Textbox(label="π Translation")
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],
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title="π§ Mini NLP AI Assistant",
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description="Analyze sentiment, detect named entities, and translate to Hindi or French using Hugging Face Transformers."
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)
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iface.launch()
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requirements.txt
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transformers
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gradio
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