app.py file

app.py

@@ -0,0 +1,22 @@
+# app.py
+import gradio as gr
+from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+
+model_name = "distilbert-base-uncased"
+tokenizer = DistilBertTokenizer.from_pretrained(model_name)
+model = DistilBertForSequenceClassification.from_pretrained(model_name)
+
+def predict_sentiment(text):
+    # Tokenize and predict
+    inputs = tokenizer(text, return_tensors="pt")
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class = logits.argmax().item()
+
+    # Return the predicted class
+    return {"positive": logits[0][1].item(), "negative": logits[0][0].item()}
+
+iface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(),
+    outputs="label",