Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from langdetect import detect
+import requests
+import wikipedia
+
+# Load multilingual NER model
+ner_pipeline = pipeline("ner", model="Davlan/xlm-roberta-base-ner-hrl", grouped_entities=True)
+
+# Translation models cache
+translation_models = {}
+
+# Get Wikidata entity info via SPARQL
+def get_wikidata_info(entity, lang="en"):
+    query = f'''
+    SELECT ?item ?itemLabel ?itemDescription WHERE {{
+      ?item rdfs:label "{entity}"@{lang}.
+      SERVICE wikibase:label {{ bd:serviceParam wikibase:language "{lang}". }}
+    }} LIMIT 1
+    '''
+    url = "https://query.wikidata.org/sparql"
+    headers = {"Accept": "application/sparql-results+json"}
+    try:
+        response = requests.get(url, params={"query": query}, headers=headers)
+        data = response.json()
+        if data['results']['bindings']:
+            item = data['results']['bindings'][0]
+            label = item.get('itemLabel', {}).get('value', entity)
+            description = item.get('itemDescription', {}).get('value', '')
+            return label, description
+    except:
+        pass
+    return entity, ""
+
+# Get Wikipedia description as fallback
+def get_wikipedia_summary(entity, lang="en"):
+    try:
+        wikipedia.set_lang(lang)
+        summary = wikipedia.summary(entity, sentences=2, auto_suggest=True, redirect=True)
+        return summary
+    except:
+        return "No description available."
+
+# Translate text using MarianMT models
+def translate_text(text, src_lang, tgt_lang):
+    if src_lang == tgt_lang:
+        return text
+    model_name = f"Helsinki-NLP/opus-mt-{src_lang}-{tgt_lang}"
+    try:
+        if model_name not in translation_models:
+            translation_models[model_name] = pipeline("translation", model=model_name)
+        translator = translation_models[model_name]
+        return translator(text, max_length=256)[0]['translation_text']
+    except:
+        return text  # Return untranslated if model fails
+
+# Combined NER + Wikidata + fallback Wikipedia + translation
+def multilingual_entity_info(text, output_lang):
+    try:
+        detected_lang = detect(text)
+    except:
+        detected_lang = "en"
+
+    entities = ner_pipeline(text)
+    seen = set()
+    result = f"**🌐 Detected Language:** `{detected_lang}`\n**🌍 Output Language:** `{output_lang}`\n\n"
+
+    for ent in entities:
+        name = ent['word'].strip()
+        if name not in seen and name.isalpha():
+            seen.add(name)
+            label, desc = get_wikidata_info(name, lang=detected_lang)
+            if not desc:
+                desc = get_wikipedia_summary(name, lang=detected_lang)
+            translated_desc = translate_text(desc, detected_lang, output_lang)
+            result += f"\n---\n\n## 🔎 {label}\n\n{translated_desc}\n"
+
+    return result if seen else "No named entities found."
+
+# Gradio UI with output language selector
+iface = gr.Interface(
+    fn=multilingual_entity_info,
+    inputs=[
+        gr.Textbox(lines=4, placeholder="Type any sentence in any language..."),
+        gr.Dropdown(label="Select Output Language", choices=["en", "hi", "es", "fr", "de", "ta", "zh"], value="en")
+    ],
+    outputs=gr.Markdown(),
+    title="🌐 Multilingual NER + Wikidata + Wikipedia",
+    description="Detects entities in any language, fetches descriptions from Wikidata (or Wikipedia), and translates the output into your chosen language."
+)
+
+if __name__ == "__main__":
+    iface.launch()