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app.py

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

requirements.txt

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+transformers
+gradio