Create app.py

app.py

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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()