Create app.py

app.py

@@ -0,0 +1,38 @@
+import gradio as gr
+import torch
+from transformers import BertTokenizer, BertForSequenceClassification
+
+model_path = "my_model"
+
+tokenizer = BertTokenizer.from_pretrained(model_path)
+model = BertForSequenceClassification.from_pretrained(model_path)
+
+device = torch.device("cpu")
+model.to(device)
+model.eval()
+
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    logits = outputs.logits
+    probs = torch.softmax(logits, dim=1)
+
+    predicted_class = torch.argmax(probs, dim=1).item()
+    confidence = probs[0][predicted_class].item()
+
+    label = "Positive 😊" if predicted_class == 1 else "Negative 😡"
+
+    return f"{label} (Confidence: {confidence:.2f})"
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(lines=3, placeholder="Enter text..."),
+    outputs="text",
+    title="🎬 Sentiment Analyzer",
+    description="BERT-based sentiment classifier"
+)
+
+demo.launch()