Update app.py

app.py

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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()