app.py

import streamlit as st
from teapotai import TeapotAI, TeapotAISettings
import hashlib
import os
import requests
from langsmith import traceable

default_documents = []

API_KEY = os.environ.get("brave_api_key")

def brave_search(query, count=3):
    url = "https://api.search.brave.com/res/v1/web/search"
    headers = {"Accept": "application/json", "X-Subscription-Token": API_KEY}
    params = {
        "q": query, 
        "count": count
    }
    
    response = requests.get(url, headers=headers, params=params)
    
    if response.status_code == 200:
        results = response.json().get("web", {}).get("results", [])
        print(results)
        return [(res["title"], res["description"], res["url"]) for res in results]
    else:
        print(f"Error: {response.status_code}, {response.text}")
        return []

@traceable 
def query_teapot(prompt, context, user_input, teapot_ai):
    response = teapot_ai.query(
        context=prompt+"\n"+context,
        query=user_input
    )

    return response
    

# Function to handle the chat with TeapotAI
def handle_chat(user_input, teapot_ai):
    results = brave_search(user_input)

    documents = []
    for i, (title, description, url) in enumerate(results, 1):
        documents.append(description.replace('<strong>','').replace('</strong>',''))
    print(documents)

    context="\n".join(documents)
    prompt = "You are Teapot, an open-source AI assistant optimized for low-end devices, providing short, accurate responses without hallucinating while excelling at information extraction and text summarization."
    response = query_teapot(context, user_input, teapot_ai)
    
    # response = teapot_ai.chat([
    #     {
    #         "role": "system",
    #         "content": "You are Teapot, an open-source AI assistant optimized for running efficiently on low-end devices. You provide short, accurate responses without hallucinating and excel at extracting information and summarizing text."
    #     },
    #     {
    #         "role": "user",
    #         "content": user_input
    #     }
    # ])
    return response

def suggestion_button(suggestion_text, teapot_ai):
    if st.button(suggestion_text):
        handle_chat(suggestion_text, teapot_ai)

# Function to hash documents
def hash_documents(documents):
    return hashlib.sha256("\n".join(documents).encode("utf-8")).hexdigest()

# Streamlit app
def main():
    st.set_page_config(page_title="TeapotAI Chat", page_icon=":robot_face:", layout="wide")

    # Sidebar for document input
    st.sidebar.header("Document Input (for RAG)")
    user_documents = st.sidebar.text_area(
        "Enter documents, each on a new line",
        value="\n".join(default_documents)
    )

    # Parse the user input to get the documents (split by newline)
    documents = user_documents.split("\n")
    
    # Ensure non-empty documents
    documents = [doc for doc in documents if doc.strip()]

    # Check if documents have changed
    new_documents_hash = hash_documents(documents)

    # Load model if documents have changed, otherwise reuse the model from session_state
    if "documents_hash" not in st.session_state or st.session_state.documents_hash != new_documents_hash:
        with st.spinner('Loading Model and Embeddings...'):
            teapot_ai = TeapotAI(documents=documents or default_documents, settings=TeapotAISettings(rag_num_results=3))
        
        # Store the new hash and model in session state
        st.session_state.documents_hash = new_documents_hash
        st.session_state.teapot_ai = teapot_ai
    else:
        # Reuse the existing model
        teapot_ai = st.session_state.teapot_ai

    # Initialize session state and display the welcome message
    if "messages" not in st.session_state:
        st.session_state.messages = [{"role": "assistant", "content": "Hi, I am Teapot AI, how can I help you?"}]
    
    # Display previous messages from chat history
    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])

    # Accept user input
    user_input = st.chat_input("Ask about famous landmarks")

    s1, s2, s3 = st.columns([1, 2, 3])
    with s1:
        suggestion_button("How tall is the Eiffel Tower?", teapot_ai)
    with s2:
        suggestion_button("Extract the year the Eiffel Tower was constructed.", teapot_ai)
    with s3:
        suggestion_button("How large is the Death Star?", teapot_ai)
    
    if user_input:
        # Display user message in chat message container
        with st.chat_message("user"):
            st.markdown(user_input)

        # Add user message to session state
        st.session_state.messages.append({"role": "user", "content": user_input})
        with st.spinner('Generating Response...'):
            # Get the answer from TeapotAI using chat functionality
            response = handle_chat(user_input, teapot_ai)

        # Display assistant response in chat message container
        with st.chat_message("assistant"):
            st.markdown(response)

        # Add assistant response to session state
        st.session_state.messages.append({"role": "assistant", "content": response})
        st.markdown("### Suggested Questions")

        

# Run the app
if __name__ == "__main__":
    main()