Update app.py

app.py

@@ -9,21 +9,37 @@ from io import BytesIO
 # --- 1. Load Recipes Dataset ---
 @st.cache_data
 def load_recipes():
-    dataset = load_dataset("recipes.csv", split="train")  
-    recipes_df = dataset.to_pandas()  
+    try:
+        # Load CSV using Pandas instead of Hugging Face dataset loader
+        recipes_df = pd.read_csv("recipes.csv")
+
+        # Rename columns to match expected names
+        recipes_df = recipes_df.rename(columns={"recipe_name": "title", "directions": "instructions"})
+
+        # Keep only necessary columns
+        recipes_df = recipes_df[['title', 'ingredients', 'instructions']]
+        recipes_df.fillna("", inplace=True)
+
+        # Clean ingredients text
+        recipes_df["ingredients"] = recipes_df["ingredients"].str.lower().str.replace(r'[^\w\s]', '', regex=True)
+        recipes_df["combined_text"] = recipes_df["title"] + " " + recipes_df["ingredients"]
+        
+        return recipes_df
+
+    except Exception as e:
+        st.error(f"⚠️ Error loading recipes: {e}")
+        return pd.DataFrame()  # Return empty DataFrame in case of failure
 
-    # Rename columns to match expected ones
-    recipes_df = recipes_df.rename(columns={"recipe_name": "title", "directions": "instructions"})
+# Load the recipes before using them
+recipes_df = load_recipes()
 
-    # Keep only required columns
-    recipes_df = recipes_df[['title', 'ingredients', 'instructions']]
-    recipes_df.fillna("", inplace=True)
+# Ensure `recipes_df` is not empty before iterating over it
+if not recipes_df.empty:
+    for index, row in recipes_df.iterrows():
+        print(row)  # Replace with actual processing logic
+else:
+    st.warning("⚠️ No recipes found. Please check the CSV file.")
 
-    # Clean the ingredients column
-    recipes_df["ingredients"] = recipes_df["ingredients"].str.lower().str.replace(r'[^\w\s]', '', regex=True)
-    recipes_df["combined_text"] = recipes_df["title"] + " " + recipes_df["ingredients"]
-    
-    return recipes_df
 
 
 # --- 2. Setup SentenceTransformer Model for Embeddings ---