Upload app.py

app.py

@@ -1,64 +1,120 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-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
+
+# 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()