Remove unused chat history logging script

chat_history/app - Copy.py

@@ -1,242 +0,0 @@
-import os
-import time
-import streamlit as st
-from dotenv import load_dotenv
-from langchain_groq import ChatGroq
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_community.vectorstores import FAISS
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_community.document_loaders import WebBaseLoader
-from langchain_core.prompts import PromptTemplate
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.runnables import RunnableLambda
-import requests
-import json
-
-# Логирует взаимодействие в JSON-файл
-from datetime import datetime
-
-
-def log_interaction(user_input: str, bot_response: str):
-    """Логирует взаимодействие в JSON-файл"""
-    log_entry = {
-        "timestamp": datetime.now().isoformat(),
-        "user_input": user_input,
-        "bot_response": bot_response
-    }
-    
-    log_dir = "chat_history"
-    os.makedirs(log_dir, exist_ok=True)
-    
-    log_path = os.path.join(log_dir, "chat_logs.json")
-    with open(log_path, "a") as f:
-        f.write(json.dumps(log_entry) + "\n")
-
-#
-
-
-
-# Page configuration
-st.set_page_config(page_title="Status Law Assistant", page_icon="⚖️")
-
-# Knowledge base info in session_state
-if 'kb_info' not in st.session_state:
-    st.session_state.kb_info = {
-        'build_time': None,
-        'size': None
-    }
-
-# Display title and knowledge base info
-# st.title("www.Status.Law Legal Assistant")
-
-st.markdown(
-    '''
-    <h1>
-        ⚖️ 
-        <a href="https://status.law/" style="text-decoration: underline; color: blue; font-size: inherit;">
-            Status.Law
-        </a> 
-        Legal Assistant
-    </h1>
-    ''',
-    unsafe_allow_html=True
-)
-
-if st.session_state.kb_info['build_time'] and st.session_state.kb_info['size']:
-    st.caption(f"(Knowledge base build time: {st.session_state.kb_info['build_time']:.2f} seconds, "
-               f"size: {st.session_state.kb_info['size']:.2f} MB)")
-
-# Path to store vector database
-VECTOR_STORE_PATH = "vector_store"
-
-# Создание папки истории, если она не существует
-if not os.path.exists("chat_history"):
-    os.makedirs("chat_history")
-
-# Website URLs
-urls = [
-    "https://status.law",  
-    "https://status.law/about",
-    "https://status.law/careers",  
-    "https://status.law/tariffs-for-services-of-protection-against-extradition",
-    "https://status.law/challenging-sanctions",
-    "https://status.law/law-firm-contact-legal-protection"
-    "https://status.law/cross-border-banking-legal-issues", 
-    "https://status.law/extradition-defense", 
-    "https://status.law/international-prosecution-protection", 
-    "https://status.law/interpol-red-notice-removal",  
-    "https://status.law/practice-areas",  
-    "https://status.law/reputation-protection",
-    "https://status.law/faq"
-]
-
-# Load secrets
-try:
-    GROQ_API_KEY = st.secrets["GROQ_API_KEY"]
-except Exception as e:
-    st.error("Error loading secrets. Please check your configuration.")
-    st.stop()
-
-# Initialize models
-@st.cache_resource
-def init_models():
-    llm = ChatGroq(
-        model_name="llama-3.3-70b-versatile",
-        temperature=0.6,
-        api_key=GROQ_API_KEY
-    )
-    embeddings = HuggingFaceEmbeddings(
-        model_name="intfloat/multilingual-e5-large-instruct"
-    )
-    return llm, embeddings
-
-# Build knowledge base
-def build_knowledge_base(embeddings):
-    start_time = time.time()
-    
-    documents = []
-    with st.status("Loading website content...") as status:
-        for url in urls:
-            try:
-                loader = WebBaseLoader(url)
-                docs = loader.load()
-                documents.extend(docs)
-                status.update(label=f"Loaded {url}")
-            except Exception as e:
-                st.error(f"Error loading {url}: {str(e)}")
-                
-    text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size=500,
-        chunk_overlap=100
-    )
-    chunks = text_splitter.split_documents(documents)
-    
-    vector_store = FAISS.from_documents(chunks, embeddings)
-    vector_store.save_local(VECTOR_STORE_PATH)
-    
-    end_time = time.time()
-    build_time = end_time - start_time
-    
-    # Calculate knowledge base size
-    total_size = 0
-    for path, dirs, files in os.walk(VECTOR_STORE_PATH):
-        for f in files:
-            fp = os.path.join(path, f)
-            total_size += os.path.getsize(fp)
-    size_mb = total_size / (1024 * 1024)
-    
-    # Save knowledge base info
-    st.session_state.kb_info['build_time'] = build_time
-    st.session_state.kb_info['size'] = size_mb
-    
-    st.success(f"""
-    Knowledge base created successfully:
-    - Time taken: {build_time:.2f} seconds
-    - Size: {size_mb:.2f} MB
-    - Number of chunks: {len(chunks)}
-    """)
-    
-    return vector_store
-
-# Main function
-def main():
-    # Initialize models
-    llm, embeddings = init_models()
-    
-    # Check if knowledge base exists
-    if not os.path.exists(VECTOR_STORE_PATH):
-        st.warning("Knowledge base not found.")
-        if st.button("Create Knowledge Base"):
-            vector_store = build_knowledge_base(embeddings)
-            st.session_state.vector_store = vector_store
-            st.rerun()
-    else:
-        if 'vector_store' not in st.session_state:
-            st.session_state.vector_store = FAISS.load_local(
-                VECTOR_STORE_PATH,
-                embeddings,
-                allow_dangerous_deserialization=True
-            )
-    
-    # Chat mode
-    if 'vector_store' in st.session_state:
-        if 'messages' not in st.session_state:
-            st.session_state.messages = []
-            
-        # Display chat history
-        for message in st.session_state.messages:
-            st.chat_message("user").write(message["question"])
-            st.chat_message("assistant").write(message["answer"])
-            
-        # User input
-        if question := st.chat_input("Ask your question"):
-            st.chat_message("user").write(question)
-            
-            # Retrieve context and generate response
-            with st.chat_message("assistant"):
-                with st.spinner("Thinking..."):
-                    context = st.session_state.vector_store.similarity_search(question)
-                    context_text = "\n".join([doc.page_content for doc in context])
-                    
-                    prompt = PromptTemplate.from_template("""
-                    You are a helpful and polite legal assistant at Status Law.
-                    You answer in the language in which the question was asked.
-                    Answer the question based on the context provided.
-                    If you cannot answer based on the context, say so politely and offer to contact Status Law directly via the following channels:
-                    - For all users: +32465594521 (landline phone).
-                    - For English and Swedish speakers only: +46728495129 (available on WhatsApp, Telegram, Signal, IMO).
-                    - Provide a link to the contact form: [Contact Form](https://status.law/law-firm-contact-legal-protection/).
-                    If the user has questions about specific services and their costs, suggest they visit the page https://status.law/tariffs-for-services-of-protection-against-extradition-and-international-prosecution/ for detailed information.
-
-                    Ask the user additional questions to understand which service to recommend and provide an estimated cost. For example, clarify their situation and needs to suggest the most appropriate options.
-
-                    Also, offer free consultations if they are available and suitable for the user's request.
-                    Answer professionally but in a friendly manner.
-
-                    Example:
-                    Q: How can I challenge the sanctions?
-                    A: To challenge the sanctions, you should consult with our legal team, who specialize in this area. Please contact us directly for detailed advice. You can fill out our contact form here: [Contact Form](https://status.law/law-firm-contact-legal-protection/).
-
-                    Context: {context}
-                    Question: {question}
-                    """)
-                    
-                    chain = prompt | llm | StrOutputParser()
-                    response = chain.invoke({
-                        "context": context_text,
-                        "question": question
-                    })
-                    
-                    st.write(response)
-                    
-                    
-                    # В блоке генерации ответа (после st.write(response))   
-                    log_interaction(question, response)
-                    # Save chat history
-                    st.session_state.messages.append({
-                        "question": question,
-                        "answer": response
-                    })
-
-if __name__ == "__main__":
-    main()