Update app.py

app.py

@@ -1,4 +1,4 @@
-############ it works , الحمد لله
+
 import os
 from huggingface_hub import login
 from datasets import load_dataset
@@ -8,13 +8,11 @@ from huggingface_hub import hf_hub_download
 import chromadb
 from sentence_transformers import SentenceTransformer
 
-import os
-from huggingface_hub import login
 
 # Charger le token depuis les secrets
 hf_token = os.getenv("HF_TOKEN")  # Assurez-vous que 'HF_TOKEN' est bien le nom du secret Hugging Face
 
-# Connecte-toi à Hugging Face
+# Connecting à Hugging Face
 login(hf_token)
 # Charger le dataset
 dataset = load_dataset("Maryem2025/dataset-train")  # Changez le nom si nécessaire
@@ -55,8 +53,7 @@ class VectorStore:
         texts = [
             f"Name: {name}. Ingredients: {ingr}. Instructions: {instr}. Cuisine: {cui}. Total time: {total} minutes."
             for name, ingr, instr, cui, total in zip(names, ingredients, instructions, cuisine, total_time)
-            #f"Name: {name}. Ingredients: {ingr}. Instructions: {instr}."
-            #for name, ingr, instr in zip(names, ingredients, instructions)           
+                   
         ]
 
         # Ajouter les embeddings au store de vecteurs
@@ -111,12 +108,12 @@ demo = gr.Interface(
         gr.Textbox(lines=2, placeholder="Enter your message here...", label="Message"),
     ],
     outputs=gr.Textbox(label="Generated Text"),
-    title="Chatbot - Your Personal Culinary Advisor: Discover What to Cook Next!",
+    title="FALFOUL'S KITCHEN",
     description="Running LLM with context retrieval from ChromaDB",
     examples=[
         ["I have leftover rice, what can I make out of it?"],
         ["I just have some milk and chocolate, what dessert can I make?"],
-        ["I am allergic to coconut milk, what can I use instead in a Thai curry?"],
+       
         ["Can you suggest a vegan breakfast recipe?"],
         ["How do I make a perfect scrambled egg?"],
         ["Can you guide me through making a soufflé?"],