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import gradio as gr
from transformers import pipeline
from PIL import Image
import os
# Sentiment analysis pipeline (small model)
emotion_pipeline = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", top_k=1)
# Only two emotions and 4 apps each
app_suggestions = {
"sadness": [
("Messenger", "icons/Messenger.png"),
("Spotify", "icons/Spotify.png"),
("Reddit", "icons/tiktok.png"),
("Headspace", "icons/Youtube.png"),
],
"joy": [
("Instagram", "icons/Blackboard.png"),
("YouTube", "icons/Outlook.png"),
("TikTok", "icons/UC.png"),
("Snapchat", "icons/Word.png"),
]
}
def analyze_day(text):
result = emotion_pipeline(text)[0][0]
emotion = result['label'].lower()
if emotion not in app_suggestions:
return f"Detected Emotion: **{emotion.capitalize()}**\n\nNo suggestions available.", []
suggestions = app_suggestions[emotion]
images = []
for name, path in suggestions:
try:
img = Image.open(path).resize((80, 80))
images.append(gr.update(value=img, visible=True))
except Exception as e:
images.append(gr.update(visible=False))
# Pad with None if fewer than 4
while len(images) < 4:
images.append(gr.update(visible=False))
app_output = f"Detected Emotion: **{emotion.capitalize()}**\n\nSuggested Apps:"
return (app_output, *images)
with gr.Blocks() as demo:
gr.Markdown("## 😊😢 How Do you feel today?")
gr.Markdown(
"This app uses emotion detection to understand whether you're feeling **happy** or **sad**.\n\n"
"If you're **happy**, it recommends **productivity apps**.\n"
"If you're **sad**, it recommends **entertainment apps** to lift your mood. 😊"
)
user_input = gr.Textbox(lines=3, placeholder="Type something like 'I feel so happy today!'", label="Your day")
output_text = gr.Markdown()
with gr.Row() as output_gallery:
img1 = gr.Image(label="", width=80, height=80, visible=False)
img2 = gr.Image(label="", width=80, height=80, visible=False)
img3 = gr.Image(label="", width=80, height=80, visible=False)
img4 = gr.Image(label="", width=80, height=80, visible=False)
submit_btn = gr.Button("Analyze & Suggest Apps")
submit_btn.click(fn=analyze_day,
inputs=user_input,
outputs=[output_text, img1, img2, img3, img4])
demo.launch(share=True) |