Upload app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+pipe = pipeline(model="delarosajav95/tw-roberta-base-sentiment-FT-v2")
+#function that Gradio will use to classify
+def classify_text(inputs):
+  result = pipe(inputs, return_all_scores=True)
+  output = []
+  label_mapping = {"LABEL_0": "Negative", "LABEL_1": "Neutral", "LABEL_2": "Positive"}
+  for i, predictions in enumerate(result):
+    for pred in predictions:
+      label = label_mapping.get(pred['label'], pred['label'])
+      score = pred['score']
+      output.append(f"{label}: {score:.2%}")
+
+  return "\n".join(output)
+#defining Gradio interface
+textbox = gr.Textbox(lines=3, placeholder="Enter a user review, comment, or opinion to evaluate...(e.g., 'I love this product! It looks great.')",
+                     label="User Review/Comment:")
+
+output_box = gr.Textbox(label="Results:")
+
+iface = gr.Interface(
+    fn=classify_text, 
+    inputs=textbox,
+    outputs=output_box,
+    live=True, 
+    title="Sentiment Analysis for User Opinions & Feedback",
+    allow_flagging="never",
+)
+
+# Launch the interface
+iface.launch()