File size: 3,951 Bytes
bb730d1 a6a8e5c edbb892 a6a8e5c edbb892 bb730d1 edbb892 a6a8e5c edbb892 b744666 edbb892 a6a8e5c b744666 a6a8e5c edbb892 a6a8e5c edbb892 a6a8e5c edbb892 a6a8e5c edbb892 a6a8e5c edbb892 a6a8e5c edbb892 a6a8e5c edbb892 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 |
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.") |