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import gradio as gr |
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import plotly.express as px |
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import pandas as pd |
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from transformers import pipeline |
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classifier = pipeline(task="text-classification", model="AR04/Senti", top_k=None) |
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id2label = { |
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0: "admiration", 1: "amusement", 2: "anger", 3: "annoyance", 4: "approval", |
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5: "caring", 6: "confusion", 7: "curiosity", 8: "desire", 9: "disappointment", |
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10: "disapproval", 11: "disgust", 12: "embarrassment", 13: "excitement", |
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14: "fear", 15: "gratitude", 16: "grief", 17: "joy", 18: "love", 19: "nervousness", |
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20: "optimism", 21: "pride", 22: "realization", 23: "relief", 24: "remorse", |
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25: "sadness", 26: "surprise", 27: "neutral" |
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} |
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def classify_and_visualize(text): |
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outputs = classifier([text])[0] |
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df = pd.DataFrame(outputs) |
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fig = px.line_polar( |
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df, |
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r="score", |
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theta="label", |
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line_close=True, |
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title="Emotion Radar", |
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) |
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fig.update_traces(fill="toself", line=dict(color="blue")) |
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return df.to_dict("records"), fig |
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with gr.Blocks() as demo: |
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gr.Markdown("## 🎭 Emotion Radar Chat App") |
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with gr.Row(): |
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with gr.Column(): |
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text_input = gr.Textbox(placeholder="Type a message...", lines=2) |
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submit_btn = gr.Button("Analyze") |
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with gr.Column(): |
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result_json = gr.JSON() |
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result_plot = gr.Plot() |
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submit_btn.click(fn=classify_and_visualize, inputs=text_input, outputs=[result_json, result_plot]) |
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if __name__ == "__main__": |
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demo.launch() |
