import os
import gradio as gr
from llama_cpp import Llama
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.prompts import PromptTemplate

class RAGInterface:
    def __init__(self):
        # Initialize embedding model
        self.embeddings = HuggingFaceEmbeddings(
            model_name="sentence-transformers/all-MiniLM-L6-v2",
            model_kwargs={'device': 'cpu'},
            encode_kwargs={'normalize_embeddings': True}
        )
        
        # Load vector store
        persist_directory = os.path.join(os.path.dirname(__file__), 'mydb')
        self.vectorstore = Chroma(
            persist_directory=persist_directory,
            embedding_function=self.embeddings
        )
        
        # Initialize LLM
        self.llm = Llama.from_pretrained(
            repo_id="bartowski/Llama-3.2-1B-Instruct-GGUF",
            filename="Llama-3.2-1B-Instruct-Q8_0.gguf",
        )
        
        # Define RAG prompt template
        self.template = """Answer the question based only on the following context:
        {context}
        
        Question: {question}
        
        Answer the question in a clear way. If you cannot find the answer in the context, 
        just say "I don't have enough information to answer this question."
        
        Make sure to:
        1. Only use information from the provided context
        2. If you're unsure, acknowledge it
        """
        self.prompt = PromptTemplate.from_template(self.template)

    def respond(self, message, history, system_message, max_tokens, temperature):
        # Build messages list
        messages = [{"role": "system", "content": system_message}]
        for user_msg, assistant_msg in history:
            if user_msg:
                messages.append({"role": "user", "content": user_msg})
            if assistant_msg:
                messages.append({"role": "assistant", "content": assistant_msg})

        # Search vector store
        retriever = self.vectorstore.as_retriever(search_kwargs={"k": 5})
        docs = retriever.get_relevant_documents(message)
        context = "\n\n".join([doc.page_content for doc in docs])

        # Format prompt and add to messages
        final_prompt = self.prompt.format(context=context, question=message)
        messages.append({"role": "user", "content": final_prompt})

        # Generate response
        response = self.llm.create_chat_completion(
            messages=messages,
            max_tokens=max_tokens,
            temperature=temperature,
        )

        return response['choices'][0]['message']['content']

    def create_interface(self):
        # Custom CSS for better styling
        custom_css = """
        <style>
            /* Global Styles */
            body, #root {
                font-family: Helvetica, Arial, sans-serif;
                background-color: #1a1a1a;
                color: #fafafa;
            }
            
            /* Header Styles */
            .app-header {
                background: linear-gradient(45deg, #1a1a1a 0%, #333333 100%);
                padding: 24px;
                border-radius: 8px;
                margin-bottom: 24px;
                text-align: center;
            }
            
            .app-title {
                font-size: 36px;
                margin: 0;
                color: #fafafa;
            }
            
            .app-subtitle {
                font-size: 18px;
                margin: 8px 0;
                color: #fafafa;
                opacity: 0.8;
            }
            
            /* Chat Container */
            .chat-container {
                background-color: #2a2a2a;
                border-radius: 8px;
                padding: 20px;
                margin-bottom: 20px;
            }
            
            /* Control Panel */
            .control-panel {
                background-color: #333;
                padding: 16px;
                border-radius: 8px;
                margin-top: 16px;
            }
            
            /* Gradio Component Overrides */
            .gr-button {
                background-color: #4a4a4a;
                color: #fff;
                border: none;
                border-radius: 4px;
                padding: 8px 16px;
                transition: background-color 0.3s;
            }
            
            .gr-button:hover {
                background-color: #5a5a5a;
            }
            
            .gr-input, .gr-dropdown {
                background-color: #3a3a3a;
                color: #fff;
                border: 1px solid #4a4a4a;
                border-radius: 4px;
                padding: 8px;
            }
        </style>
        """

        # Header HTML
        header_html = f"""
        <div class="app-header">
            <h1 class="app-title">Document-Based Question Answering</h1>
            <h2 class="app-subtitle">Powered by Llama and RAG</h2>
        </div>
        {custom_css}
        """

        # Create Gradio interface
        demo = gr.ChatInterface(
            fn=self.respond,
            additional_inputs=[
                gr.Textbox(
                    value="You are a friendly chatbot.",
                    label="System Message",
                    elem_classes="control-panel"
                ),
                gr.Slider(
                    minimum=1,
                    maximum=2048,
                    value=512,
                    step=1,
                    label="Max New Tokens",
                    elem_classes="control-panel"
                ),
                gr.Slider(
                    minimum=0.1,
                    maximum=1.0,
                    value=0.7,
                    step=0.1,
                    label="Temperature",
                    elem_classes="control-panel"
                ),
            ],
            title="",  # Title is handled in custom HTML
            description="Ask questions about your documents and get AI-powered answers.",
            examples=[
                "What is a Computer?",
                "How does machine learning work?",
                "Explain artificial intelligence.",
            ],
            theme=gr.themes.Default(),
        )

        # Wrap the interface with custom HTML
        return gr.Blocks(css=custom_css) as wrapped_demo:
            gr.HTML(header_html)
            demo.render()

def main():
    interface = RAGInterface()
    demo = interface.create_interface()
    demo.launch(debug=True)

if __name__ == "__main__":
    main()