Create app.py

app.py

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+import torch
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# --- Load model ---
+MODEL_NAME = "tabularisai/multilingual-sentiment-analysis"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+
+# --- Sentiment analysis function ---
+def analyze_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    sentiment_map = {
+        0: "Very Negative",
+        1: "Negative",
+        2: "Neutral",
+        3: "Positive",
+        4: "Very Positive"
+    }
+    prediction = torch.argmax(probabilities, dim=-1).item()
+    return sentiment_map.get(prediction, "Unknown")
+
+# --- Gradio UI ---
+demo = gr.Interface(
+    fn=analyze_sentiment,
+    inputs=gr.Textbox(lines=4, placeholder="Enter your sentence..."),
+    outputs="text",
+    title="Sentiment Analyzer",
+    description="Enter a sentence to analyze its sentiment using a multilingual model."
+)
+
+demo.launch()