Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+from sentence_transformers import SentenceTransformer
+import faiss
+
+# 1. Dataset — replace or load from CSV
+texts = [
+    "Creativity is intelligence having fun.",
+    "Simplicity is the ultimate sophistication.",
+    "Innovation distinguishes between a leader and a follower.",
+    # Add more...
+]
+df = pd.DataFrame({"text": texts})
+
+# 2. Embeddings + FAISS index
+model = SentenceTransformer("all-MiniLM-L6-v2")
+emb = model.encode(df["text"].tolist(), convert_to_numpy=True).astype("float32")
+faiss.normalize_L2(emb)
+index = faiss.IndexFlatIP(emb.shape[1])
+index.add(emb)
+
+# 3. Search function
+def search(query):
+    qv = model.encode([query], convert_to_numpy=True).astype("float32")
+    faiss.normalize_L2(qv)
+    D, I = index.search(qv, 3)  # top 3
+    return "\n".join([df.iloc[i]["text"] for i in I[0]])
+
+# 4. Gradio UI
+demo = gr.Interface(
+    fn=search,
+    inputs=gr.Textbox(lines=2, placeholder="Type something..."),
+    outputs="text",
+    title="Mini Quote Finder",
+    description="Type a phrase, get similar quotes."
+)
+
+demo.launch()