Initial files commit

app.py

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+import gradio as gr
+from menu_chromadb_semantic_search import search_dishes
+
+iface = gr.Interface(
+    fn=search_dishes,
+    inputs=gr.Textbox(label="Search for a dish"),
+    outputs=gr.Textbox(label="Top 5 Similar Dishes"),
+    title="Menu Item Semantic Search",
+    description="Type a dish name to find the top 5 similar menu items."
+)
+
+iface.launch()

menu_chromadb_semantic_search.py

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+import chromadb
+from chromadb.utils import embedding_functions
+import csv
+
+# --- Setup ChromaDB (in-memory for Hugging Face Spaces free tier) ---
+chroma_client = chromadb.Client()
+
+# SentenceTransformer embedding function
+sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(
+    model_name="all-MiniLM-L6-v2"
+)
+
+# Create collection
+collection = chroma_client.create_collection(
+    name="my_collection",
+    embedding_function=sentence_transformer_ef
+)
+
+# --- Load CSV data ---
+with open("menu_items.csv") as file:
+    lines = csv.reader(file)
+    documents = []
+    metadatas = []
+    ids = []
+
+    for i, line in enumerate(lines):
+        if i == 0:
+            continue  # skip header
+        documents.append(line[1])
+        metadatas.append({"item_id": line[0]})
+        ids.append(str(i))
+
+# Add to ChromaDB
+collection.add(
+    documents=documents,
+    metadatas=metadatas,
+    ids=ids
+)
+
+def search_dishes(query: str) -> str:
+    """Search for top 5 similar dishes."""
+    results = collection.query(
+        query_texts=[query],
+        n_results=5,
+        include=["documents", "metadatas"]
+    )
+    hits = results["documents"][0]
+    ids_meta = results["metadatas"][0]
+    output = [f"{ids_meta[i]['item_id']}: {hits[i]}" for i in range(len(hits))]
+    return "\n".join(output)

requirements.txt

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+gradio
+chromadb
+sentence-transformers