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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() |