import gradio as gr
from transformers import pipeline
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
import os

# Load the embedding model
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")

# Load the pre-existing vector database
persist_directory = "db"
vectordb = Chroma(persist_directory=persist_directory, embedding_function=embeddings)

# Load the Marco-o1 model
pipe = pipeline("text-generation", model="AIDC-AI/Marco-o1", device_map="auto", torch_dtype="auto", trust_remote_code=True)

def get_relevant_context(query, k=3):
    # Search the vector database for relevant documents
    docs = vectordb.similarity_search(query, k=k)
    # Combine the relevant documents into a single context string
    context = "\n".join([doc.page_content for doc in docs])
    return context

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [system_message]
    
    # Get relevant context from the vector database
    context = get_relevant_context(message)
    
    # Add context to the system message
    if context:
        messages[0] = f"{system_message}\n\nRelevant context:\n{context}"
    
    for val in history:
        if val[0]:
            messages.append(val[0])
        if val[1]:
            messages.append(val[1])

    messages.append(message)
    
    # Combine all messages into one string
    input_text = "\n".join(messages)
    
    response = pipe(
        input_text,
        max_length=max_tokens + len(input_text),
        temperature=temperature,
        top_p=top_p,
        num_return_sequences=1
    )[0]['generated_text']
    
    # Extract new response
    new_response = response[len(input_text):].strip()
    
    yield new_response

demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(
            value="You are a helpful AI assistant. Use the provided context to answer questions accurately.",
            label="System message"
        ),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
    title="Marco-O1 Assistant with Knowledge Base",
    description="Ask questions about the documents in the knowledge base. The assistant will use the relevant context to provide accurate answers."
)

if __name__ == "__main__":
    demo.launch()