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app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from deep_translator import GoogleTranslator
+from langdetect import detect
+import torch
+
+# Load model & tokenizer
+MODEL_NAME = "cardiffnlp/twitter-roberta-base-sentiment"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+
+# Sentiment labels
+emotion_labels = {
+    0: "Negative 😕",
+    1: "Neutral 😐",
+    2: "Positive 🙂"
+}
+
+# Translator
+translator = GoogleTranslator(source="auto", target="en")
+
+def analyze_sentiment(user_input):
+    # Detect language
+    detected_language = detect(user_input)
+    
+    # Translate if not English
+    translated_text = translator.translate(user_input) if detected_language != "en" else user_input
+    
+    # Tokenize
+    inputs = tokenizer(translated_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    logits = outputs.logits
+    predicted_class = torch.argmax(logits, dim=-1).item()
+    emotion = emotion_labels.get(predicted_class, "Unknown")
+
+    return {
+        "Original Text": user_input,
+        "Translated Text": translated_text,
+        "Detected Language": detected_language,
+        "Predicted Sentiment": emotion
+    }
+
+# Build Gradio UI
+iface = gr.Interface(
+    fn=analyze_sentiment,
+    inputs=gr.Textbox(lines=3, placeholder="Enter text in any language..."),
+    outputs="json",
+    title="🌍 Multilingual Sentiment Analysis",
+    description="Enter text in any language. The system will auto-detect, translate to English, and predict sentiment (Positive/Neutral/Negative)."
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+transformers
+torch
+deep-translator
+langdetect
+gradio