src/streamlit_app.py

import streamlit as st
import os
from langchain_groq import ChatGroq
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_text_splitters import CharacterTextSplitter # Updated import for modern LangChain
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory

# 1. Setup Page
st.set_page_config(page_title="Serenity AI", page_icon="🌿")
st.title("🌿 Serenity: Your CBT Companion")

# 2. Sidebar - Privacy & Resources
with st.sidebar:
    st.header("About")
    st.info("This is a support tool, NOT a doctor. Data is processed locally for privacy.")
    groq_api_key = st.text_input("Groq API Key", type="password")

# 3. Load & Process Knowledge Base (Only runs once)
@st.cache_resource
def setup_rag():
    # --- THE FIX: Use absolute path to find the file safely ---
    current_dir = os.path.dirname(os.path.abspath(__file__))
    file_path = os.path.join(current_dir, "cbt_resources.txt")
    
    # Check if file exists to avoid crashing
    if not os.path.exists(file_path):
        st.error(f"Error: Could not find 'cbt_resources.txt' at {file_path}")
        return None

    with open(file_path, "r") as f:
        raw_text = f.read()
    
    # Split into chunks
    text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50)
    texts = text_splitter.split_text(raw_text)
    
    # Create Embeddings (Local & Free)
    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
    
    # Store in Vector DB
    db = Chroma.from_texts(texts, embeddings)
    return db.as_retriever()

# 4. Initialize Chat Logic
if groq_api_key:
    # Setup LLM
    # Setup LLM
    try:
        llm = ChatGroq(
            temperature=0.6, 
            # UPDATED MODEL NAME BELOW:
            model_name="llama-3.3-70b-versatile", 
            groq_api_key=groq_api_key
        )
        
        # Setup Memory
        if "memory" not in st.session_state:
            st.session_state.memory = ConversationBufferMemory(
                memory_key="chat_history",
                return_messages=True
            )

        # Setup RAG Chain
        retriever = setup_rag()
        
        if retriever:
            chain = ConversationalRetrievalChain.from_llm(
                llm=llm,
                retriever=retriever,
                memory=st.session_state.memory,
                verbose=False
            )
            
            # 5. Chat Interface
            if "messages" not in st.session_state:
                st.session_state.messages = [{"role": "assistant", "content": "Hello. I'm Serenity. How are you feeling today?"}]

            for msg in st.session_state.messages:
                st.chat_message(msg["role"]).write(msg["content"])

            if prompt := st.chat_input():
                st.session_state.messages.append({"role": "user", "content": prompt})
                st.chat_message("user").write(prompt)

                # Safety Check (Simple Keyword Filter)
                dangerous_keywords = ["suicide", "kill myself", "end it all", "die"]
                if any(word in prompt.lower() for word in dangerous_keywords):
                    response = "I'm really concerned about you, but I am an AI. Please call your local emergency number immediately (like 988 in the US). You are not alone."
                else:
                    # Generate Response using RAG
                    with st.spinner("Thinking..."):
                        response_dict = chain.invoke({"question": prompt})
                        response = response_dict['answer']

                st.session_state.messages.append({"role": "assistant", "content": response})
                st.chat_message("assistant").write(response)
        
    except Exception as e:
        st.error(f"An error occurred: {e}")

else:
    st.warning("Please enter your Groq API Key to start.")