Create main.py

main.py

@@ -0,0 +1,78 @@
+import os
+import pandas as pd
+import faiss
+import torch
+import numpy as np
+from fastapi import FastAPI
+from sentence_transformers import SentenceTransformer
+from huggingface_hub import snapshot_download
+
+REPO_ID = "abhinavsunil/kitchenelite-recipe-model"
+MODEL_CACHE = "/tmp/model_cache"
+TOP_K = 5
+
+app = FastAPI(title="KitchenElite Recipe Search API")
+
+model = None
+index = None
+df = None
+
+
+@app.on_event("startup")
+def load_assets():
+    global model, index, df
+
+    print("🚀 Downloading model repo snapshot...")
+
+    local_dir = snapshot_download(
+        repo_id=REPO_ID,
+        local_dir=MODEL_CACHE,
+        local_dir_use_symlinks=False
+    )
+
+    print("📦 Loading metadata...")
+    df = pd.read_parquet(os.path.join(local_dir, "metadata.parquet"))
+
+    print("📦 Loading FAISS index...")
+    index = faiss.read_index(os.path.join(local_dir, "recipes.index"))
+
+    print("📦 Loading SentenceTransformer model...")
+    model = SentenceTransformer(local_dir, device="cpu")
+
+    print("✅ All assets loaded successfully!")
+
+
+@app.get("/")
+def home():
+    return {"status": "KitchenElite API Running 🚀"}
+
+
+@app.get("/search")
+def search(query: str):
+    query_vector = model.encode([query])
+    faiss.normalize_L2(query_vector)
+
+    distances, indices = index.search(
+        query_vector.astype("float32"),
+        TOP_K
+    )
+
+    results = df.iloc[indices[0]]
+
+    output = []
+    for _, row in results.iterrows():
+        output.append({
+            "name": str(row["name"]),
+            "ingredients": (
+                list(row["ingredients"])
+                if isinstance(row["ingredients"], np.ndarray)
+                else row["ingredients"]
+            ),
+            "calories": float(row["calories"]),
+            "protein": float(row["protein"])
+        })
+
+    return {
+        "query": query,
+        "results": output
+    }