Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import torch.nn.functional as F
+
+# Load model and tokenizer
+model_name = "Omartificial-Intelligence-Space/SA-BERT-Classifier"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Put model in eval mode
+model.eval()
+
+# Inference function
+def classify_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probs = F.softmax(logits, dim=1).squeeze()
+
+    # Map class indices to human-readable labels if known (example below)
+    labels = ["negative", "neutral", "positive"]
+    top_class = torch.argmax(probs).item()
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=classify_sentiment,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your text here..."),
+    outputs=gr.Label(num_top_classes=3),
+    title="Arabic Sentiment Classifier (SA-BERT)"
+)
+
+interface.launch()