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import gradio as gr |
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from transformers import pipeline |
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from PIL import Image |
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import os |
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emotion_pipeline = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", top_k=1) |
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app_suggestions = { |
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"sadness": [ |
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("Messenger", "icons/Messenger.png"), |
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("Spotify", "icons/Spotify.png"), |
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("Reddit", "icons/tiktok.png"), |
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("Headspace", "icons/Youtube.png"), |
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], |
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"joy": [ |
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("Instagram", "icons/Blackboard.png"), |
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("YouTube", "icons/Outlook.png"), |
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("TikTok", "icons/UC.png"), |
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("Snapchat", "icons/Word.png"), |
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] |
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} |
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def analyze_day(text): |
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result = emotion_pipeline(text)[0][0] |
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emotion = result['label'].lower() |
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if emotion not in app_suggestions: |
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return f"Detected Emotion: **{emotion.capitalize()}**\n\nNo suggestions available.", [] |
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suggestions = app_suggestions[emotion] |
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images = [] |
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for name, path in suggestions: |
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try: |
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img = Image.open(path).resize((80, 80)) |
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images.append(gr.update(value=img, visible=True)) |
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except Exception as e: |
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images.append(gr.update(visible=False)) |
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while len(images) < 4: |
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images.append(gr.update(visible=False)) |
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app_output = f"Detected Emotion: **{emotion.capitalize()}**\n\nSuggested Apps:" |
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return (app_output, *images) |
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with gr.Blocks() as demo: |
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gr.Markdown("## 😊😢 How Do you feel today?") |
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gr.Markdown( |
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"This app uses emotion detection to understand whether you're feeling **happy** or **sad**.\n\n" |
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"If you're **happy**, it recommends **productivity apps**.\n" |
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"If you're **sad**, it recommends **entertainment apps** to lift your mood. 😊" |
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) |
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user_input = gr.Textbox(lines=3, placeholder="Type something like 'I feel so happy today!'", label="Your day") |
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output_text = gr.Markdown() |
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with gr.Row() as output_gallery: |
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img1 = gr.Image(label="", width=80, height=80, visible=False) |
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img2 = gr.Image(label="", width=80, height=80, visible=False) |
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img3 = gr.Image(label="", width=80, height=80, visible=False) |
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img4 = gr.Image(label="", width=80, height=80, visible=False) |
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submit_btn = gr.Button("Analyze & Suggest Apps") |
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submit_btn.click(fn=analyze_day, |
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inputs=user_input, |
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outputs=[output_text, img1, img2, img3, img4]) |
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demo.launch(share=True) |
