VibeGuide v1.0

.gradio/certificate.pem

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+-----BEGIN CERTIFICATE-----
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+-----END CERTIFICATE-----

app.py

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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((300, 300))
+            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)

requirements.txt

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+gradio
+transformers
+torch
+pillow
+requests