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Create app.py
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app.py
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import gradio as gr
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import random
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import time
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import pymongo
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import certifi
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import os
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from dotenv import load_dotenv
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import argparse
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from dataclasses import dataclass
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from langchain.vectorstores.chroma import Chroma
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from langchain_openai.embeddings import OpenAIEmbeddings
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from langchain_openai.chat_models import ChatOpenAI
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from langchain.prompts import ChatPromptTemplate
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from deep_translator import GoogleTranslator
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uri = "mongodb+srv://clementrof:t5fXqwpDQYFpvuCk@cluster0.rl5qhcj.mongodb.net/?retryWrites=true&w=majority"
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# Create a new client and connect to the server
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client = pymongo.MongoClient(uri, tlsCAFile=certifi.where())
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# Send a ping to confirm a successful connection
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try:
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client.admin.command('ping')
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print("Pinged your deployment. You successfully connected to MongoDB!")
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except Exception as e:
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print(e)
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# Access your database
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db = client.get_database('camila')
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records = db.info
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# Load environment variables from .env
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load_dotenv()
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# Access the private key
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private_key = os.getenv("OPENAI_API_KEY")
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os.environ["OPENAI_API_KEY"] = "OPENAI_API_KEY"
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CHROMA_PATH = "ch_chatbot"
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####### F R ################
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PROMPT_TEMPLATE = """
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Réponds à la question en te basant sur le contexte suivant :
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{context}
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---
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Voici l'historique de cette conversation, utilise l'historique comme une mémoire:
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{memory}
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---
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Réponds à la question en se basant sur le contexte ci-dessus et parle de la même manière que le contexte. Ne dis pas que tu utilises le contexte pour répondre : {question}
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"""
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def message(question,memory):
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# Prepare the DB.
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embedding_function = OpenAIEmbeddings()
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db = Chroma(persist_directory=CHROMA_PATH,
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embedding_function=embedding_function)
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# Search the DB.
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results = db.similarity_search_with_relevance_scores(question, k=3)
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if len(results) == 0 or results[0][1] < 0.7:
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print("Unable to find matching results.")
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return
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context_text = "\n\n---\n\n".join(
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[doc.page_content for doc, _score in results])
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prompt_template = ChatPromptTemplate.from_template(PROMPT_TEMPLATE)
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prompt = prompt_template.format(context=context_text, memory=memory, question=question)
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print(prompt)
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model = ChatOpenAI()
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response_text = model.invoke(prompt)
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content = response_text.content
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return content
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def Chat_call(question):
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existing_user_doc = records.find_one({'ID': '1'})
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message_log = []
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messages = existing_user_doc['message']
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if len(messages)>1:
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messages = messages[-1:]
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message_log.extend(messages)
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# Convert each dictionary into a string representation
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message_strings = [f"{message['role']}: {message['content']}" for message in message_log]
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# Join the strings with newline characters
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memory = '\n'.join(message_strings)
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response = message(question,memory)
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records.update_one({'ID': '1'},
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{'$push':{'message': {'role': 'user', 'content': f'{question}'}}})
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records.update_one({'ID': '1'},
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{'$push':{'message': {'role': 'assistant', 'content': f'{response}'}}})
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return response
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with gr.Blocks() as demo:
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chatbot = gr.Chatbot()
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msg = gr.Textbox()
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clear = gr.ClearButton([msg, chatbot])
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def respond(message, chat_history):
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bot_message = Chat_call(message)
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chat_history.append((message, bot_message))
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return "", chat_history
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msg.submit(respond, [msg, chatbot], [msg, chatbot])
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if __name__ == "__main__":
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demo.launch()
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