Add utility modules and configuration settings for chatbot application
This commit is contained in:
2
components/__init__.py
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2
components/__init__.py
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from .chatbot import process_query, reset_conversation, change_model, change_collection
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from .callbacks import GradioStreamingHandler
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12
components/callbacks.py
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components/callbacks.py
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import queue
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from langchain.callbacks.base import BaseCallbackHandler
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# Handler personnalisé pour capturer les tokens en streaming
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class GradioStreamingHandler(BaseCallbackHandler):
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def __init__(self):
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self.tokens_queue = queue.Queue()
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self.full_text = ""
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def on_llm_new_token(self, token, **kwargs):
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self.tokens_queue.put(token)
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self.full_text += token
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385
components/chatbot.py
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385
components/chatbot.py
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import traceback
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import threading
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import queue
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from langchain.prompts import ChatPromptTemplate
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from langchain_ollama import ChatOllama
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from rag_chatbot import MultimodalRAGChatbot
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from config.settings import QDRANT_URL, QDRANT_COLLECTION_NAME, EMBEDDING_MODEL, OLLAMA_URL, DEFAULT_MODEL
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from translations.lang_mappings import LANGUAGE_MAPPING
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from utils.image_utils import base64_to_image
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from langchain.callbacks.base import BaseCallbackHandler
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import re
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def clean_llm_response(text):
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"""Nettoie la réponse du LLM en enlevant les balises de pensée et autres éléments non désirés."""
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# Supprimer les blocs de pensée (<think>...</think>)
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text = re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL)
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# Supprimer les espaces supplémentaires au début de la réponse
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text = text.lstrip()
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return text
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# Handler personnalisé pour le streaming
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class GradioStreamingHandler(BaseCallbackHandler):
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def __init__(self):
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self.tokens_queue = queue.Queue()
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self.full_text = ""
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def on_llm_new_token(self, token, **kwargs):
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self.tokens_queue.put(token)
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self.full_text += token
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# Initialiser le chatbot
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rag_bot = MultimodalRAGChatbot(
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qdrant_url=QDRANT_URL,
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qdrant_collection_name=QDRANT_COLLECTION_NAME,
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ollama_model=DEFAULT_MODEL,
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embedding_model=EMBEDDING_MODEL,
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ollama_url=OLLAMA_URL
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)
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print(f"Chatbot initialisé avec modèle: {DEFAULT_MODEL}")
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# Variables globales
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current_images = []
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current_tables = []
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# Fonctions utilitaires
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def display_images(images_list=None):
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"""Crée une liste de tuples (image, caption) pour Gradio Gallery"""
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images_to_use = images_list if images_list is not None else current_images
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if not images_to_use:
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return None
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gallery = []
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for img_data in images_to_use:
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image = img_data["image"]
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if image:
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caption = f"{img_data['caption']} (Source: {img_data['source']}, Page: {img_data['page']})"
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gallery.append((image, caption))
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return gallery if gallery else None
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def display_tables(tables_list=None, language=None):
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"""Crée le HTML pour afficher les tableaux"""
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tables_to_use = tables_list if tables_list is not None else current_tables
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if not tables_to_use:
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return None
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html = ""
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for idx, table in enumerate(tables_to_use):
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table_data = table['data']
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table_html = ""
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try:
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if isinstance(table_data, str):
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if '|' in table_data:
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rows = table_data.strip().split('\n')
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table_html = '<div class="table-container"><table>'
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for i, row in enumerate(rows):
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if i == 1 and all(c in ':-|' for c in row):
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continue
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cells = row.split('|')
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if cells and cells[0].strip() == '':
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cells = cells[1:]
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if cells and cells[-1].strip() == '':
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cells = cells[:-1]
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if cells:
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is_header = (i == 0)
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table_html += '<tr>'
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for cell in cells:
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cell_content = cell.strip()
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if is_header:
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table_html += f'<th>{cell_content}</th>'
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else:
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table_html += f'<td>{cell_content}</td>'
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table_html += '</tr>'
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table_html += '</table></div>'
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else:
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table_html = f'<pre>{table_data}</pre>'
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else:
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table_html = f'<pre>{table_data}</pre>'
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except Exception as e:
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print(f"Error formatting table {idx}: {e}")
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table_html = f'<pre>{table_data}</pre>'
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html += f"""
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<div style="margin-bottom: 20px; border: 1px solid #ddd; padding: 15px; border-radius: 8px;">
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<h3>{table.get('caption', 'Tableau')}</h3>
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<p style="color:#666; font-size:0.9em;">Source: {table.get('source', 'N/A')}, Page: {table.get('page', 'N/A')}</p>
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<p><strong>Description:</strong> {table.get('description', '')}</p>
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{table_html}
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</div>
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"""
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return html if html else None
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# Fonction pour changer de modèle
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def change_model(model_name, language="Français"):
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global rag_bot
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try:
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rag_bot = MultimodalRAGChatbot(
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qdrant_url=QDRANT_URL,
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qdrant_collection_name=QDRANT_COLLECTION_NAME,
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ollama_model=model_name,
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embedding_model=EMBEDDING_MODEL,
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ollama_url=OLLAMA_URL
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)
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print(f"Modèle changé pour: {model_name}")
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return f"✅ Modèle changé pour: {model_name}"
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except Exception as e:
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print(f"Erreur lors du changement de modèle: {e}")
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return f"❌ Erreur: {str(e)}"
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# Fonction pour changer de collection
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def change_collection(collection_name, language="Français"):
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global rag_bot
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try:
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rag_bot = MultimodalRAGChatbot(
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qdrant_url=QDRANT_URL,
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qdrant_collection_name=collection_name,
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ollama_model=rag_bot.llm.model,
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embedding_model=EMBEDDING_MODEL,
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ollama_url=OLLAMA_URL
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)
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print(f"Collection changée pour: {collection_name}")
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return f"✅ Collection changée pour: {collection_name}"
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except Exception as e:
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print(f"Erreur lors du changement de collection: {e}")
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return f"❌ Erreur: {str(e)}"
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# Fonction de traitement de requête
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def process_query(message, history, streaming, show_sources, max_images, language):
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global current_images, current_tables
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if not message.strip():
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return history, "", None, None
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current_images = []
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current_tables = []
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print(f"Traitement du message: {message}")
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print(f"Streaming: {streaming}")
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try:
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if streaming:
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# Version avec streaming dans Gradio
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history = history + [(message, "")]
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# 1. Récupérer les documents pertinents
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docs = rag_bot._retrieve_relevant_documents(message)
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# 2. Préparer le contexte et l'historique
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context = rag_bot._format_documents(docs)
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history_text = rag_bot._format_chat_history()
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# 3. Préparer le prompt
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prompt_template = ChatPromptTemplate.from_template("""
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Tu es un assistant documentaire spécialisé qui utilise toutes les informations disponibles dans le contexte fourni.
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TRÈS IMPORTANT: Tu dois répondre EXCLUSIVEMENT en {language}. Ne réponds JAMAIS dans une autre langue.
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Instructions spécifiques:
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1. Pour chaque image mentionnée dans le contexte, inclue TOUJOURS dans ta réponse:
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- La légende/caption exacte de l'image
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- La source et le numéro de page
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- Une description brève de ce qu'elle montre
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2. Pour chaque tableau mentionné dans le contexte, inclue TOUJOURS:
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- Le titre/caption exact du tableau
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- La source et le numéro de page
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- Ce que contient et signifie le tableau
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3. Lorsque tu cites des équations mathématiques:
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- Utilise la syntaxe LaTeX exacte comme dans le document ($...$ ou $$...$$)
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- Reproduis-les fidèlement sans modification
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4. IMPORTANT: Ne pas inventer d'informations - si une donnée n'est pas explicitement fournie dans le contexte,
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indique clairement que cette information n'est pas disponible dans les documents fournis.
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5. Cite précisément les sources pour chaque élément d'information (format: [Source, Page]).
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6. CRUCIAL: Ta réponse doit être UNIQUEMENT et INTÉGRALEMENT en {language}, quelle que soit la langue de la question.
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Historique de conversation:
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{chat_history}
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Contexte (à utiliser pour répondre):
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{context}
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Question: {question}
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Réponds de façon structurée et précise en intégrant activement les images, tableaux et équations disponibles dans le contexte.
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Ta réponse doit être exclusivement en {language}.
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""")
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# 4. Formater les messages pour le LLM
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messages = prompt_template.format_messages(
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chat_history=history_text,
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context=context,
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question=message,
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language=LANGUAGE_MAPPING.get(language, "français")
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)
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# 5. Créer un handler de streaming personnalisé
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handler = GradioStreamingHandler()
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# 6. Créer un modèle LLM avec notre handler
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streaming_llm = ChatOllama(
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model=rag_bot.llm.model,
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base_url=rag_bot.llm.base_url,
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streaming=True,
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callbacks=[handler]
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)
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# 7. Lancer la génération dans un thread pour ne pas bloquer l'UI
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def generate_response():
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streaming_llm.invoke(messages)
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thread = threading.Thread(target=generate_response)
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thread.start()
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# 8. Récupérer les tokens et mettre à jour l'interface
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partial_response = ""
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# Attendre les tokens avec un timeout
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while thread.is_alive() or not handler.tokens_queue.empty():
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try:
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token = handler.tokens_queue.get(timeout=0.05)
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partial_response += token
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# Nettoyer la réponse uniquement pour l'affichage (pas pour l'historique interne)
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clean_response = clean_llm_response(partial_response)
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history[-1] = (message, clean_response)
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yield history, "", None, None
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except queue.Empty:
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continue
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# Après la boucle, nettoyer la réponse complète pour l'historique interne
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partial_response = clean_llm_response(partial_response)
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rag_bot.chat_history.append({"role": "user", "content": message})
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rag_bot.chat_history.append({"role": "assistant", "content": partial_response})
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# 10. Récupérer les sources, images, tableaux
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texts, images, tables = rag_bot._process_documents(docs)
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# Préparer les informations sur les sources
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source_info = ""
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if texts:
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source_info += f"📚 {len(texts)} textes • "
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if images:
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source_info += f"🖼️ {len(images)} images • "
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if tables:
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source_info += f"📊 {len(tables)} tableaux"
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if source_info:
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source_info = "Sources trouvées: " + source_info
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# 11. Traiter les images
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if show_sources and images:
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images = images[:max_images]
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for img in images:
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img_data = img.get("image_data")
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if img_data:
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image = base64_to_image(img_data)
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if image:
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current_images.append({
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"image": image,
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"caption": img.get("caption", ""),
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"source": img.get("source", ""),
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"page": img.get("page", ""),
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"description": img.get("description", "")
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})
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# 12. Traiter les tableaux
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if show_sources and tables:
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for table in tables:
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current_tables.append({
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"data": rag_bot.format_table(table.get("table_data", "")),
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"caption": table.get("caption", ""),
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"source": table.get("source", ""),
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"page": table.get("page", ""),
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"description": table.get("description", "")
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})
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# 13. Retourner les résultats finaux
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images_display = display_images()
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tables_display = display_tables()
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yield history, source_info, images_display, tables_display
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else:
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# Version sans streaming
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print("Mode non-streaming activé")
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source_info = ""
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result = rag_bot.chat(message, stream=False)
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# Nettoyer la réponse des balises <think>
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result["response"] = clean_llm_response(result["response"])
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history = history + [(message, result["response"])]
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# Mise à jour de l'historique interne
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rag_bot.chat_history.append({"role": "user", "content": message})
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rag_bot.chat_history.append({"role": "assistant", "content": result["response"]})
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# Traiter les sources
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if "texts" in result:
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source_info += f"📚 {len(result['texts'])} textes • "
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if "images" in result:
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source_info += f"🖼️ {len(result['images'])} images • "
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if "tables" in result:
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source_info += f"📊 {len(result['tables'])} tableaux"
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if source_info:
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source_info = "Sources trouvées: " + source_info
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# Traiter les images et tableaux
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if show_sources and "images" in result and result["images"]:
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images = result["images"][:max_images]
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for img in images:
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img_data = img.get("image_data")
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if img_data:
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image = base64_to_image(img_data)
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if image:
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current_images.append({
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"image": image,
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"caption": img.get("caption", ""),
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"source": img.get("source", ""),
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"page": img.get("page", ""),
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"description": img.get("description", "")
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})
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if show_sources and "tables" in result and result["tables"]:
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tables = result["tables"]
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for table in tables:
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current_tables.append({
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"data": rag_bot.format_table(table.get("table_data", "")),
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"caption": table.get("caption", ""),
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"source": table.get("source", ""),
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"page": table.get("page", ""),
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"description": table.get("description", "")
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})
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yield history, source_info, display_images(), display_tables()
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except Exception as e:
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error_msg = f"Une erreur est survenue: {str(e)}"
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traceback_text = traceback.format_exc()
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print(error_msg)
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print(traceback_text)
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history = history + [(message, error_msg)]
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yield history, "Erreur lors du traitement de la requête", None, None
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# Fonction pour réinitialiser la conversation
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def reset_conversation():
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global current_images, current_tables
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current_images = []
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current_tables = []
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rag_bot.clear_history()
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return [], "", None, None
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198
components/ui.py
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198
components/ui.py
Normal file
@@ -0,0 +1,198 @@
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import gradio as gr
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from config.settings import DEFAULT_MODEL, QDRANT_COLLECTION_NAME, AVAILABLE_MODELS
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from translations.lang_mappings import UI_TRANSLATIONS, UI_SUPPORTED_LANGUAGES
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from utils.katex_script import KATEX_CSS_JS
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def update_ui_language_elements(language):
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"""Met à jour les éléments de l'interface utilisateur en fonction de la langue sélectionnée"""
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pass # Implémentez selon vos besoins
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def build_interface(
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process_query_fn,
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reset_conversation_fn,
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change_model_fn,
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change_collection_fn,
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update_ui_language_fn
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):
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"""Construit l'interface utilisateur avec Gradio."""
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with gr.Blocks(css=KATEX_CSS_JS, theme=gr.themes.Soft(primary_hue="blue")) as interface:
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gr.Markdown("# 📚 Assistant documentaire intelligent")
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with gr.Row():
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with gr.Column(scale=2):
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# Chatbot principal
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chat_interface = gr.Chatbot(
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height=600,
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show_label=False,
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layout="bubble",
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elem_id="chatbot"
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)
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with gr.Row():
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msg = gr.Textbox(
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show_label=False,
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placeholder="Posez votre question...",
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container=False,
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scale=4
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)
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submit_btn = gr.Button("Envoyer", variant="primary", scale=1)
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clear_btn = gr.Button("Effacer la conversation")
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source_info = gr.Markdown("", elem_id="sources_info")
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with gr.Column(scale=1):
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with gr.Accordion("Options", open=True):
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# Sélecteur de modèle
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model_selector = gr.Dropdown(
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choices=AVAILABLE_MODELS,
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value=DEFAULT_MODEL,
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label="Modèle Ollama",
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info="Choisir le modèle de language à utiliser"
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)
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model_status = gr.Markdown(f"Modèle actuel: **{DEFAULT_MODEL}**")
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|
||||
# Sélecteur de langue
|
||||
language_selector = gr.Dropdown(
|
||||
choices=UI_SUPPORTED_LANGUAGES,
|
||||
value=UI_SUPPORTED_LANGUAGES[0],
|
||||
label="Langue des réponses",
|
||||
info="Choisir la langue dans laquelle l'assistant répondra"
|
||||
)
|
||||
|
||||
# Sélecteur de collection Qdrant
|
||||
collection_name_input = gr.Textbox(
|
||||
value=QDRANT_COLLECTION_NAME,
|
||||
label="Collection Qdrant",
|
||||
info="Nom de la collection de documents à utiliser"
|
||||
)
|
||||
collection_status = gr.Markdown(f"Collection actuelle: **{QDRANT_COLLECTION_NAME}**")
|
||||
|
||||
# Bouton d'application de la collection
|
||||
apply_collection_btn = gr.Button("Appliquer la collection")
|
||||
|
||||
streaming = gr.Checkbox(
|
||||
label="Mode streaming",
|
||||
value=True,
|
||||
info="Voir les réponses s'afficher progressivement"
|
||||
)
|
||||
show_sources = gr.Checkbox(label="Afficher les sources", value=True)
|
||||
max_images = gr.Slider(
|
||||
minimum=1,
|
||||
maximum=10,
|
||||
value=3,
|
||||
step=1,
|
||||
label="Nombre max d'images"
|
||||
)
|
||||
|
||||
gr.Markdown("---")
|
||||
|
||||
gr.Markdown("### 🖼️ Images pertinentes")
|
||||
image_gallery = gr.Gallery(
|
||||
label="Images pertinentes",
|
||||
show_label=False,
|
||||
columns=2,
|
||||
height=300,
|
||||
object_fit="contain"
|
||||
)
|
||||
|
||||
gr.Markdown("### 📊 Tableaux")
|
||||
tables_display = gr.HTML()
|
||||
|
||||
# Connecter le changement de modèle
|
||||
model_selector.change(
|
||||
fn=change_model_fn,
|
||||
inputs=model_selector,
|
||||
outputs=model_status
|
||||
)
|
||||
|
||||
# Connecter le changement de collection
|
||||
apply_collection_btn.click(
|
||||
fn=change_collection_fn,
|
||||
inputs=collection_name_input,
|
||||
outputs=collection_status
|
||||
)
|
||||
|
||||
# Fonction pour effacer l'entrée
|
||||
def clear_input():
|
||||
return ""
|
||||
|
||||
# Configuration des actions principales
|
||||
msg.submit(
|
||||
process_query_fn,
|
||||
inputs=[msg, chat_interface, streaming, show_sources, max_images, language_selector],
|
||||
outputs=[chat_interface, source_info, image_gallery, tables_display]
|
||||
).then(clear_input, None, msg)
|
||||
|
||||
submit_btn.click(
|
||||
process_query_fn,
|
||||
inputs=[msg, chat_interface, streaming, show_sources, max_images, language_selector],
|
||||
outputs=[chat_interface, source_info, image_gallery, tables_display]
|
||||
).then(clear_input, None, msg)
|
||||
|
||||
clear_btn.click(
|
||||
reset_conversation_fn,
|
||||
outputs=[chat_interface, source_info, image_gallery, tables_display]
|
||||
)
|
||||
|
||||
# Style KaTeX et amélioration du design
|
||||
gr.Markdown("""
|
||||
<style>
|
||||
.gradio-container {max-width: 1200px !important}
|
||||
#chatbot {height: 600px; overflow-y: auto;}
|
||||
#sources_info {margin-top: 10px; color: #666;}
|
||||
|
||||
/* Improved styles for equations */
|
||||
.katex { font-size: 1.1em !important; }
|
||||
.math-inline { background: #f8f9fa; padding: 2px 5px; border-radius: 4px; }
|
||||
.math-display { background: #f8f9f9; margin: 10px 0; padding: 10px; border-radius: 5px; overflow-x: auto; text-align: center; }
|
||||
|
||||
/* Table styles */
|
||||
table {
|
||||
border-collapse: collapse;
|
||||
width: 100%;
|
||||
margin: 15px 0;
|
||||
font-size: 0.9em;
|
||||
}
|
||||
table, th, td {
|
||||
border: 1px solid #ddd;
|
||||
}
|
||||
th, td {
|
||||
padding: 8px 12px;
|
||||
text-align: left;
|
||||
}
|
||||
th {
|
||||
background-color: #f2f2f2;
|
||||
}
|
||||
tr:nth-child(even) {
|
||||
background-color: #f9f9f9;
|
||||
}
|
||||
.table-container {
|
||||
overflow-x: auto;
|
||||
margin-top: 10px;
|
||||
}
|
||||
</style>
|
||||
|
||||
<!-- Loading KaTeX -->
|
||||
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.8/dist/katex.min.css">
|
||||
<script src="https://cdn.jsdelivr.net/npm/katex@0.16.8/dist/katex.min.js"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/katex@0.16.8/dist/contrib/auto-render.min.js"></script>
|
||||
|
||||
<script>
|
||||
// Script pour rendre les équations mathématiques avec KaTeX
|
||||
document.addEventListener('DOMContentLoaded', function() {
|
||||
setTimeout(function() {
|
||||
if (window.renderMathInElement) {
|
||||
renderMathInElement(document.body, {
|
||||
delimiters: [
|
||||
{left: '$$', right: '$$', display: true},
|
||||
{left: '$', right: '$', display: false}
|
||||
],
|
||||
throwOnError: false
|
||||
});
|
||||
}
|
||||
}, 1000);
|
||||
});
|
||||
</script>
|
||||
""")
|
||||
|
||||
return interface
|
||||
Reference in New Issue
Block a user