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import os
import streamlit as st
from dotenv import load_dotenv
from langchain.chains import create_history_aware_retriever, create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_community.vectorstores import Chroma
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
import pandas as pd
from huggingface_hub import login
# Load environment variables
openai_api_key = os.getenv("OPENAI_API_KEY")
hf_key = os.getenv("huggingface")
login(hf_key)
# Load product data from CSV
product_data_path = "./db/catalog_chatbot_2024-07-08.csv"
df = pd.read_csv(product_data_path, encoding='ISO-8859-1', sep=';')
# Define the embedding model
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
# Create a persistent directory for ChromaDB
persistent_directory = os.path.join("./","db", "chroma_open_ai")
# Check if the vector store already exists
if not os.path.exists(os.path.join(persistent_directory, 'chroma.sqlite3')):
db = Chroma(persist_directory=persistent_directory, embedding_function=embeddings)
for index, row in df.iterrows():
product_info = (
f"Nom du produit: {row['Nom du produit']} - "
f"Catégorie: {row['Catégorie par défaut']} - "
f"Caractéristiques: {row['Caractéristiques']} - "
f"Prix: {row['Prix de vente TTC']} - "
f"Description: {row['Description sans HTML']}"
)
metadata = {
"reference": row['Référence interne'],
"name": row['Nom du produit'],
"price": row['Prix de vente TTC'],
"product_url": row['URL Produit']
}
db.add_texts(texts=[product_info], metadatas=[metadata])
db.persist()
else:
db = Chroma(persist_directory=persistent_directory, embedding_function=embeddings)
# Create a retriever
retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 10})
# Create a ChatOpenAI model
llm = ChatOpenAI(model="gpt-4o")
# Function to format the products
def format_retrieved_products(retrieved_docs):
recommendations = []
seen_products = set()
for doc in retrieved_docs:
metadata = doc.metadata
product_name = metadata.get("name", "Produit inconnu")
price = metadata.get("price", "Prix non disponible")
product_url = metadata.get("product_url", "#")
if product_name not in seen_products:
recommendation = f"**{product_name}** - {price} €\n[Voir produit]({product_url})"
recommendations.append(recommendation)
seen_products.add(product_name)
return "\n".join(recommendations)
# Update the system prompt
qa_system_prompt = (
"You are a sales assistant helping customers purchase wine. "
"Use the retrieved context from the Chroma DB to answer the question. "
"Recommend 3 different items and provide the URLs of the 3 products from Calais Vins."
)
qa_prompt = ChatPromptTemplate.from_messages(
[
("system", qa_system_prompt),
MessagesPlaceholder("chat_history"),
("human", "{input}"),
("system", "{context}")
]
)
question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
# Define a retrieval chain
def create_custom_retrieval_chain(retriever, llm_chain):
def invoke(inputs):
query = inputs["input"]
retrieved_docs = retriever.get_relevant_documents(query)
formatted_response = format_retrieved_products(retrieved_docs)
return {"answer": formatted_response}
return invoke
rag_chain = create_custom_retrieval_chain(retriever, question_answer_chain)
# Streamlit App Interface
def run_streamlit_chatbot():
st.title("Wine Sales Assistant")
chat_history = []
# User input area
user_query = st.text_input("Posez une question au chatbot (e.g., je recherche un vin blanc fruité):")
if user_query:
result = rag_chain({"input": user_query, "chat_history": chat_history})
# Display chatbot response
st.write("### Chatbot's Recommendations:")
st.write(result["answer"])
# Display recommendations in a pop-up like fashion
with st.expander("Voir les recommandations"):
st.write(result["answer"])
# Main function to run the Streamlit app
if __name__ == "__main__":
run_streamlit_chatbot()
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