import os
import gradio as gr
from langchain.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.chains import RetrievalQA
from langchain_together import Together

# 🔐 Set your API key
os.environ["TOGETHER_API_KEY"] = os.environ.get("TOGETHER_API_KEY", "")


# 🔁 Caches
qa_cache = {}
retriever_cache = {}

# 🔍 Load and embed the website content
def load_url(url):
    try:
        loader = WebBaseLoader(url)
        docs = loader.load()
        splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
        chunks = splitter.split_documents(docs)
        embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
        db = FAISS.from_documents(chunks, embedding=embeddings)
        retriever = db.as_retriever()
        llm = Together(
            model="mistralai/Mistral-7B-Instruct-v0.2",
            temperature=0.5,
            max_tokens=512
        )
        qa = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
        return retriever, qa, "✅ Website loaded. You can start chatting!"
    except Exception as e:
        return None, None, f"❌ Error: {str(e)}"

# 💬 Chat handler
def chat(message, history, url):
    if url not in qa_cache:
        retriever, qa, status = load_url(url)
        if retriever is None:
            history.append({"role": "user", "content": message})
            history.append({"role": "assistant", "content": status})
            return history, ""
        retriever_cache[url] = retriever
        qa_cache[url] = qa
        history.append({"role": "system", "content": status})
    else:
        qa = qa_cache[url]

    try:
        result = qa.invoke({"query": message})["result"]
    except Exception as e:
        result = f"❌ Error: {str(e)}"

    history.append({"role": "user", "content": message})
    history.append({"role": "assistant", "content": result})
    return history, ""

# ✅ Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("## 🧠 Chat with Any Website")

    url_input = gr.Textbox(label="Website URL", placeholder="https://en.wikipedia.org/wiki/LangChain")
    chatbot = gr.Chatbot(label="Chat", type="messages")
    msg_input = gr.Textbox(show_label=False, placeholder="Ask your question here and press Enter...")
    state = gr.State([])

    msg_input.submit(chat, inputs=[msg_input, state, url_input], outputs=[chatbot, msg_input])

    # 👇 Footer
    gr.Markdown(
        """
        ---
        <center>
            🔗 <a href="https://github.com/vivekreddy1105" target="_blank">GitHub</a> | 
            💼 <a href="https://www.linkedin.com/in/vivekreddy1105/" target="_blank">LinkedIn</a><br>
            © 2025 Vivek Reddy Eluka
        </center>
        """,
        elem_id="footer"
    )

demo.launch(share=True)