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import os
from huggingface_hub import login
from datasets import load_dataset
import gradio as gr
from llama_cpp import Llama
from huggingface_hub import hf_hub_download
import chromadb
from sentence_transformers import SentenceTransformer
# Authentification via un secret
hf_token = os.getenv("HF_TOKEN") # Récupérer le token depuis les secrets
login(hf_token)
# Charger le dataset
dataset = load_dataset("Maryem2025/dataset-train") # Changez le nom si nécessaire
# Initialisation du modèle Llama
llm = Llama(
model_path=hf_hub_download(
repo_id="TheBloke/CapybaraHermes-2.5-Mistral-7B-GGUF",
filename="capybarahermes-2.5-mistral-7b.Q2_K.gguf",
),
n_ctx=2048,
n_gpu_layers=50, # Ajustez selon votre VRAM
)
# Initialisation de ChromaDB Vector Store
class VectorStore:
def __init__(self, collection_name):
self.embedding_model = SentenceTransformer('sentence-transformers/multi-qa-MiniLM-L6-cos-v1')
self.chroma_client = chromadb.Client()
# Supprimer la collection existante si elle existe
if collection_name in self.chroma_client.list_collections():
self.chroma_client.delete_collection(collection_name)
# Créer une nouvelle collection
self.collection = self.chroma_client.create_collection(name=collection_name)
def populate_vectors(self, dataset):
# Sélectionner les colonnes pertinentes à concaténer
names = dataset['train']['name'][:20]
ingredients = dataset['train']['ingredients'][:20]
instructions = dataset['train']['instructions'][:20]
cuisine = dataset['train']['cuisine'][:20]
total_time = dataset['train']['total_time'][:20]
# Concaténer les textes à partir des colonnes sélectionnées
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)
]
# Ajouter les embeddings au store de vecteurs
for i, item in enumerate(texts):
embeddings = self.embedding_model.encode(item).tolist()
self.collection.add(embeddings=[embeddings], documents=[item], ids=[str(i)])
def search_context(self, query, n_results=1):
query_embedding = self.embedding_model.encode([query]).tolist()
results = self.collection.query(query_embeddings=query_embedding, n_results=n_results)
return results['documents']
# Initialisation du store de vecteurs et peuplement
dataset = load_dataset('Maryem2025/dataset-test')
vector_store = VectorStore("embedding_vector")
vector_store.populate_vectors(dataset)
# Fonction pour générer du texte
def generate_text(message, max_tokens, temperature, top_p):
# Récupérer le contexte depuis le store de vecteurs
context_results = vector_store.search_context(message, n_results=1)
context = context_results[0] if context_results else ""
# Créer le modèle de prompt
prompt_template = (
f"SYSTEM: You are a recipe generating bot.\n"
f"SYSTEM: {context}\n"
f"USER: {message}\n"
f"ASSISTANT:\n"
)
# Générer le texte avec le modèle de langue
output = llm(
prompt_template,
temperature=0.3,
top_p=0.95,
top_k=40,
repeat_penalty=1.1,
max_tokens=600,
)
# Traiter la sortie
input_string = output['choices'][0]['text'].strip()
cleaned_text = input_string.strip("[]'").replace('\\n', '\n')
continuous_text = '\n'.join(cleaned_text.split('\n'))
return continuous_text
# Définir l'interface Gradio
demo = gr.Interface(
fn=generate_text,
inputs=[
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!",
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?"],
],
cache_examples=False,
)
if __name__ == "__main__":
demo.launch()
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