Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from langdetect import detect, LangDetectException
+
+# Load model from HuggingFace
+classifier = pipeline(
+    "text-classification",
+    model="Keshav0308/multilingual-topic-classifier"
+)
+
+TOPIC_EMOJIS = {
+    "geography": "🌍",
+    "science/technology": "🔬",
+    "entertainment": "🎬",
+    "politics": "🏛️",
+    "health": "🏥",
+    "travel": "✈️",
+    "sports": "⚽"
+}
+
+LANGUAGE_NAMES = {
+    "en": "English", "fr": "French", "de": "German", "es": "Spanish",
+    "it": "Italian", "pt": "Portuguese", "ru": "Russian", "zh-cn": "Chinese",
+    "ja": "Japanese", "ko": "Korean", "ar": "Arabic", "hi": "Hindi",
+    "bn": "Bengali", "ur": "Urdu", "tr": "Turkish", "pl": "Polish",
+    "nl": "Dutch", "sv": "Swedish", "fi": "Finnish", "da": "Danish",
+    "uk": "Ukrainian", "cs": "Czech", "ro": "Romanian", "hu": "Hungarian",
+    "th": "Thai", "vi": "Vietnamese", "id": "Indonesian", "ms": "Malay",
+    "fa": "Persian", "he": "Hebrew", "pa": "Punjabi", "ta": "Tamil",
+    "te": "Telugu", "mr": "Marathi", "gu": "Gujarati", "kn": "Kannada",
+    "ml": "Malayalam", "si": "Sinhala", "ne": "Nepali", "am": "Amharic",
+    "sw": "Swahili", "yo": "Yoruba", "ig": "Igbo", "ha": "Hausa",
+    "zu": "Zulu", "af": "Afrikaans", "sq": "Albanian", "hy": "Armenian",
+    "az": "Azerbaijani", "eu": "Basque", "be": "Belarusian", "bs": "Bosnian",
+    "bg": "Bulgarian", "ca": "Catalan", "hr": "Croatian", "et": "Estonian",
+    "gl": "Galician", "ka": "Georgian", "el": "Greek", "is": "Icelandic",
+    "lv": "Latvian", "lt": "Lithuanian", "mk": "Macedonian", "mt": "Maltese",
+    "sr": "Serbian", "sk": "Slovak", "sl": "Slovenian", "cy": "Welsh",
+}
+
+def detect_language(text):
+    try:
+        code = detect(text)
+        return LANGUAGE_NAMES.get(code, f"Unknown ({code})")
+    except LangDetectException:
+        return "Could not detect"
+
+def classify_topic(text):
+    if not text or not text.strip():
+        return "", "", ""
+    
+    result = classifier(text)[0]
+    topic = result["label"]
+    confidence = result["score"] * 100
+    language = detect_language(text)
+    
+    emoji = TOPIC_EMOJIS.get(topic, "📌")
+    topic_display = f"{emoji} {topic.upper()}"
+    confidence_display = f"{confidence:.2f}%"
+    language_display = f"🌐 {language}"
+    
+    return topic_display, confidence_display, language_display
+
+# Example inputs
+examples = [
+    ["The patient was diagnosed with pneumonia and prescribed antibiotics."],
+    ["El equipo ganó el campeonato mundial de fútbol."],
+    ["Le parlement a voté une nouvelle loi sur l'environnement."],
+    ["scientists discovered a new exoplanet orbiting a distant star."],
+    ["ਕ੍ਰਿਕੇਟ ਟੀਮ ਨੇ ਵਿਸ਼ਵ ਕੱਪ ਜਿੱਤਿਆ।"],
+    ["東京オリンピックで日本が金メダルを獲得した。"],
+    ["Der Bundestag hat ein neues Klimaschutzgesetz verabschiedet."],
+]
+
+# Build UI
+with gr.Blocks(theme=gr.themes.Soft(), title="Multilingual Topic Classifier") as demo:
+    gr.Markdown("""
+    # 🌍 Multilingual Topic Classifier
+    ### Classify text into topics across 205 languages
+    Built with `xlm-roberta-base` fine-tuned on the SIB-200 dataset.
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            text_input = gr.Textbox(
+                label="Enter text in any language",
+                placeholder="Type or paste text here...",
+                lines=4
+            )
+            submit_btn = gr.Button("🔍 Classify", variant="primary", size="lg")
+        
+        with gr.Column(scale=1):
+            topic_output = gr.Textbox(label="📌 Topic", interactive=False)
+            confidence_output = gr.Textbox(label="📊 Confidence", interactive=False)
+            language_output = gr.Textbox(label="🌐 Detected Language", interactive=False)
+    
+    gr.Examples(
+        examples=examples,
+        inputs=text_input,
+        label="Try these examples"
+    )
+    
+    submit_btn.click(
+        fn=classify_topic,
+        inputs=text_input,
+        outputs=[topic_output, confidence_output, language_output]
+    )
+    
+    text_input.submit(
+        fn=classify_topic,
+        inputs=text_input,
+        outputs=[topic_output, confidence_output, language_output]
+    )
+
+demo.launch()