Create app.py

app.py

@@ -0,0 +1,33 @@
+import gradio as gr
+import faiss
+import numpy as np
+import pandas as pd
+from sentence_transformers import SentenceTransformer
+
+# Load model embedding
+embed_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+
+# Load FAISS index
+index = faiss.read_index("laptop_index.faiss")
+embeddings = np.load("laptop_embeddings.npy")
+
+# Load dataset
+df = pd.read_csv("laptop_data.csv")
+
+# Function untuk rekomendasi
+def recommend_laptops(query, top_k=5):
+    query_embedding = embed_model.encode([query])
+    D, I = index.search(query_embedding, top_k)
+    results = df.iloc[I[0]][["Company", "TypeName", "CPU_Type", "GPU_Company", "Price (Euro)"]]
+    return results.to_dict(orient="records")
+
+# Gradio UI
+demo = gr.Interface(
+    fn=recommend_laptops,
+    inputs="text",
+    outputs="json",
+    title="Laptop Recommender",
+    description="Masukkan spesifikasi yang Anda inginkan untuk rekomendasi laptop."
+)
+
+demo.launch()