app.py

import os
import streamlit as st
import json
from datetime import datetime, timedelta
from src.helper import download_hugging_face_embeddings
from langchain_community.vectorstores import Pinecone
from langchain_openai import OpenAI
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from dotenv import load_dotenv
from src.prompt import system_prompt

# Set up cache directories
os.environ['TRANSFORMERS_CACHE'] = '/tmp/model_cache'
os.environ['HF_HOME'] = '/tmp/model_cache'
os.makedirs('/tmp/model_cache', exist_ok=True)

# Load environment variables
load_dotenv()

# Rate limiting configuration
RATE_LIMIT_FILE = "/tmp/rate_limits.json"
MAX_REQUESTS_PER_DAY = 5

# Initialize rate limiting storage
def init_rate_limiting():
    if not os.path.exists(RATE_LIMIT_FILE):
        with open(RATE_LIMIT_FILE, 'w') as f:
            json.dump({}, f)

# Check if a user has exceeded their daily limit
def check_rate_limit(user_id):
    today = datetime.now().strftime('%Y-%m-%d')
    
    try:
        with open(RATE_LIMIT_FILE, 'r') as f:
            rate_limits = json.load(f)
    except (json.JSONDecodeError, FileNotFoundError):
        rate_limits = {}
    
    # Clean up old entries
    yesterday = (datetime.now() - timedelta(days=1)).strftime('%Y-%m-%d')
    users_to_remove = []
    for uid in rate_limits:
        if yesterday in rate_limits[uid]:
            del rate_limits[uid][yesterday]
            if not rate_limits[uid]:  # If user has no other days, remove them
                users_to_remove.append(uid)
    
    for uid in users_to_remove:
        del rate_limits[uid]
    
    # Check and update current user's limit
    if user_id not in rate_limits:
        rate_limits[user_id] = {}
    
    if today not in rate_limits[user_id]:
        rate_limits[user_id][today] = 0
    
    # Check if limit exceeded
    if rate_limits[user_id][today] >= MAX_REQUESTS_PER_DAY:
        return False, rate_limits[user_id][today]
    
    # Increment count and save
    rate_limits[user_id][today] += 1
    with open(RATE_LIMIT_FILE, 'w') as f:
        json.dump(rate_limits, f)
    
    return True, rate_limits[user_id][today]

def get_user_id():
    # For Streamlit, we'll use session_id as user identifier
    if not hasattr(st.session_state, 'user_id'):
        st.session_state.user_id = str(hash(datetime.now().strftime("%Y%m%d%H%M%S")))
    return st.session_state.user_id

def get_remaining_queries(user_id):
    today = datetime.now().strftime('%Y-%m-%d')
    
    try:
        with open(RATE_LIMIT_FILE, 'r') as f:
            rate_limits = json.load(f)
    except (json.JSONDecodeError, FileNotFoundError):
        return MAX_REQUESTS_PER_DAY
    
    count = rate_limits.get(user_id, {}).get(today, 0)
    return MAX_REQUESTS_PER_DAY - count

# Set up page configuration
st.set_page_config(
    page_title="Medical Assistant RAG Chatbot",
    page_icon="🩺",
    layout="centered"
)

# Initialize session state for chat history
if 'messages' not in st.session_state:
    st.session_state.messages = []

# Initialize rate limiting
init_rate_limiting()

# Display remaining queries
user_id = get_user_id()
remaining_queries = get_remaining_queries(user_id)
st.sidebar.write(f"Remaining queries today: {remaining_queries}/{MAX_REQUESTS_PER_DAY}")

# Check for API keys
PINECONE_API_KEY = os.environ.get('PINECONE_API_KEY')
OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')

if not PINECONE_API_KEY or not OPENAI_API_KEY:
    st.error("Missing API keys. Please set PINECONE_API_KEY and OPENAI_API_KEY environment variables.")
    st.stop()

os.environ["PINECONE_API_KEY"] = PINECONE_API_KEY
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY

# Cache the RAG chain initialization
@st.cache_resource
def initialize_rag_chain():
    try:
        st.sidebar.write("Loading embeddings model...")
        embeddings = download_hugging_face_embeddings()
        
        st.sidebar.write("Connecting to Pinecone...")
        index_name = "medprep"
        docsearch = Pinecone.from_existing_index(
            index_name=index_name,
            embedding=embeddings
        )
        
        retriever = docsearch.as_retriever(search_type="similarity", search_kwargs={"k": 3})
        
        st.sidebar.write("Initializing OpenAI...")
        llm = OpenAI(temperature=0.4, max_tokens=500)
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", system_prompt),
            ("human", "{input}")
        ])
        
        question_answer_chain = create_stuff_documents_chain(llm, prompt)
        rag_chain = create_retrieval_chain(retriever, question_answer_chain)
        
        st.sidebar.success("✅ System initialized successfully!")
        return rag_chain
    except Exception as e:
        st.sidebar.error(f"Error initializing system: {str(e)}")
        import traceback
        st.sidebar.text(traceback.format_exc())
        return None

# Main app title
st.title("Medical Assistant Chatbot")
st.write("Ask me any medical question, and I'll try to help!")

# Initialize the RAG chain
rag_chain = initialize_rag_chain()

if rag_chain is None:
    st.error("Failed to initialize the system. Please check the sidebar for error details.")
    st.stop()

# Display chat history
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

# Get user input
if prompt := st.chat_input("Ask a question..."):
    # Add user message to chat history
    st.session_state.messages.append({"role": "user", "content": prompt})
    
    # Display user message
    with st.chat_message("user"):
        st.markdown(prompt)
    
    # Check rate limit
    user_id = get_user_id()
    allowed, count = check_rate_limit(user_id)
    
    if not allowed:
        response = f"⚠️ Daily limit reached. You've used {count} queries today. Please try again tomorrow."
    else:
        # Process the query with the RAG chain
        with st.chat_message("assistant"):
            with st.spinner("Thinking..."):
                try:
                    result = rag_chain.invoke({"input": prompt})
                    response = result.get("answer", "Sorry, I couldn't find an answer to that.")
                    remaining = MAX_REQUESTS_PER_DAY - count
                    response += f"\n\n\n_You have {remaining} queries remaining today._"
                except Exception as e:
                    response = f"Error processing your request: {str(e)}"
            
            st.markdown(response)
    
    # Add assistant response to chat history
    st.session_state.messages.append({"role": "assistant", "content": response})

# Footer
st.markdown("---")
st.markdown("*This is a RAG-based medical assistant chatbot. It retrieves information from a medical knowledge base to answer your questions.*")