import streamlit as st
import re
from langchain_groq import ChatGroq
from langchain import hub
from langchain_chroma import Chroma
from langchain_community.document_loaders import WebBaseLoader
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_text_splitters import RecursiveCharacterTextSplitter
from sentence_transformers import SentenceTransformer
import bs4
import torch
import os

# Sidebar Style with Multicolored Background
sidebar_bg_style = """
    <style>
        [data-testid="stSidebar"] {
            background: linear-gradient(135deg, #ffafbd, #ffc3a0, #2193b0, #6dd5ed);
        }
    </style>
"""
st.markdown(sidebar_bg_style, unsafe_allow_html=True)

# Sidebar: Input for URL and API keys
st.sidebar.title("Settings")

# Input field for entering URL dynamically with placeholder and help text
url_input = st.sidebar.text_input("Enter Blog Post URL", placeholder="e.g., https://example.com/blog", help="Paste the full URL of the blog post you want to retrieve data from")

# Validate the URL and show a success message when correct
if url_input:
    if re.match(r"https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+", url_input):
        st.sidebar.markdown('<p style="color:green; font-weight:bold;">URL is correctly entered</p>', unsafe_allow_html=True)
    else:
        st.sidebar.markdown('<p style="color:red; font-weight:bold;">Invalid URL, please enter a valid one</p>', unsafe_allow_html=True)
else:
    st.sidebar.markdown('<p style="color:red; font-weight:bold;">URL is required</p>', unsafe_allow_html=True)

# Input fields for API keys with placeholders and helper text
api_key_1 = st.sidebar.text_input("Enter LangChain API Key", type="password", placeholder="Enter your LangChain API Key", help="Please enter a valid LangChain API key here")
api_key_2 = st.sidebar.text_input("Enter Groq API Key", type="password", placeholder="Enter your Groq API Key", help="Please enter your Groq API key here")

# Submit button for API keys with a success/warning message
if st.sidebar.button("Submit API Keys"):
    if api_key_1 and api_key_2:
        os.environ["LANGCHAIN_API_KEY"] = api_key_1
        os.environ["GROQ_API_KEY"] = api_key_2
        st.sidebar.markdown('<p style="color:green; font-weight:bold;">Both API keys are entered</p>', unsafe_allow_html=True)
    else:
        st.sidebar.markdown('<p style="color:red; font-weight:bold;">Please fill in both API keys</p>', unsafe_allow_html=True)

# Main Section with Multicolored Background
main_bg_style = """
    <style>
        body {
            background: linear-gradient(135deg, #ff9a9e, #fad0c4, #fbc2eb, #a18cd1);
            color: #333; /* Ensures text visibility */
        }
        .stTextInput input {
            color: #333; /* Ensures text color in inputs */
        }
    </style>
"""
st.markdown(main_bg_style, unsafe_allow_html=True)

# Title of the chatbot
st.markdown('<h1 style="color:#4CAF50; font-weight:bold;">🤖 Chatbot with URL-based Document Retrieval</h1>', unsafe_allow_html=True)

# Chat query input field with placeholder and help text
query = st.text_input("Ask a question based on the blog post", placeholder="Type your question here...", help="Enter a question related to the content of the blog post")

# Placeholder to display responses
if 'chat_history' not in st.session_state:
    st.session_state['chat_history'] = []

# Submit button for chat
if st.button("Submit Query"):
    if query and url_input:
        # Blog loading logic based on user input URL
        loader = WebBaseLoader(
            web_paths=(url_input,),  # Use the user-input URL
            bs_kwargs=dict(
                parse_only=bs4.SoupStrainer()  # Adjust based on the user's URL structure
            ),
        )
        docs = loader.load()

        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
        splits = text_splitter.split_documents(docs)

        # Define the embedding class
        class SentenceTransformerEmbedding:
            def __init__(self, model_name):
                self.model = SentenceTransformer(model_name)

            def embed_documents(self, texts):
                embeddings = self.model.encode(texts, convert_to_tensor=True)
                if isinstance(embeddings, torch.Tensor):
                    return embeddings.cpu().detach().numpy().tolist()  # Convert tensor to list
                return embeddings

            def embed_query(self, query):
                embedding = self.model.encode([query], convert_to_tensor=True)
                if isinstance(embedding, torch.Tensor):
                    return embedding.cpu().detach().numpy().tolist()[0]  # Convert tensor to list
                return embedding[0]

        # Initialize the embedding model
        embedding_model = SentenceTransformerEmbedding('all-MiniLM-L6-v2')

        # Initialize Chroma with the embedding class
        vectorstore = Chroma.from_documents(documents=splits, embedding=embedding_model)

        # Retrieve and generate using the relevant snippets of the blog
        retriever = vectorstore.as_retriever()
        prompt = hub.pull("rlm/rag-prompt")

        def format_docs(docs):
            return "\n\n".join(doc.page_content for doc in docs)

        rag_chain = (
            {"context": retriever | format_docs, "question": RunnablePassthrough()}
            | prompt
            | ChatGroq(model="llama3-8b-8192")  # Replace `llm` with an appropriate language model
            | StrOutputParser()
        )

        # Generate the answer using the user's query
        result = rag_chain.invoke(query)

        # Store query and response in session for chat history
        st.session_state['chat_history'].append((query, result))
    else:
        st.warning("Both URL and query are required")

# Display chat history
for q, r in st.session_state['chat_history']:
    st.write(f"**User:** {q}")
    st.write(f"**Bot:** {r}")