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app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+MODEL_NAME = "nlptown/bert-base-multilingual-uncased-sentiment"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+
+LABELS = {
+    0: "Very Negative",
+    1: "Negative",
+    2: "Neutral",
+    3: "Positive",
+    4: "Very Positive"
+}
+
+def predict_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    outputs = model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=1)
+    confidence, prediction = torch.max(probs, dim=1)
+    sentiment = LABELS[prediction.item()]
+    return {
+        "Sentiment feeling": sentiment,
+        "Confidence score": f"{confidence.item():.3f} ({'Highly Certain' if confidence.item() > 0.8 else 'Somewhat Certain' if confidence.item() > 0.6 else 'Uncertain'})"
+    }
+
+iface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(lines=2, placeholder="Enter text (any language)..."),
+    outputs="json",
+    title="🌍 Multilingual Sentiment Analysis",
+    description="Check your text's sentiment instantly using a multilingual BERT model trained on reviews. Supports languages like English, Spanish, French, German, etc.",
+    theme="soft",
+    allow_flagging="never"
+)
+
+iface.launch()

requirements.txt

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+gradio
+transformers
+torch