Update app.py

app.py

@@ -1,120 +1,129 @@
-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()
+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
+
+from dotenv import load_dotenv
+
+
+# Charger le fichier .env
+load_dotenv()
+
+# Lire le token depuis l'environnement
+hf_token = os.getenv("HF_TOKEN")
+
+# 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()