Create app.

app.

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+import gradio as gr
+from sentence_transformers import SentenceTransformer, util
+import torch
+
+# Sample quote dataset
+quotes = [
+    "You are stronger than you think.",
+    "It's okay to take a break.",
+    "Progress, not perfection.",
+    "Breathe. You’re doing better than you think.",
+    "Small steps still move you forward.",
+    "This feeling is temporary.",
+    "One day at a time.",
+    "You got this.",
+    "Your value is not measured by productivity.",
+    "Kindness starts with yourself."
+]
+
+# Load the embedding model
+model = SentenceTransformer('all-MiniLM-L6-v2')
+quote_embeddings = model.encode(quotes, convert_to_tensor=True)
+
+# Recommendation function
+def recommend_quote(user_input):
+    user_embedding = model.encode(user_input, convert_to_tensor=True)
+    similarities = util.pytorch_cos_sim(user_embedding, quote_embeddings)
+    top_results = torch.topk(similarities, k=3)
+    recommended = [quotes[i] for i in top_results.indices[0]]
+    return "\n\n".join(recommended)
+
+# Gradio UI
+interface = gr.Interface(
+    fn=recommend_quote,
+    inputs=gr.Textbox(placeholder="Type how you feel..."),
+    outputs="text",
+    title="MoodMatch",
+    description="Type how you feel. Get 3 uplifting quotes powered by AI.",
+)
+interface.launch()