import gradio as gr
import spaces
import torch
from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import HuggingFacePipeline
from langchain.chains import RetrievalQA
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

# Load and process documents
doc_loader = TextLoader("dataset.txt")
docs = doc_loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
split_docs = text_splitter.split_documents(docs)

# Create vector database
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
vectordb = FAISS.from_documents(split_docs, embeddings)

# Load model and create pipeline
model_name = "01-ai/Yi-Coder-9B-Chat"

device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")

tokenizer = AutoTokenizer.from_pretrained(model_name)
#model = AutoModelForCausalLM.from_pretrained(model_name, device_map="cuda", torch_dtype="auto")
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    device_map=device,
    torch_dtype=torch.float16 if device == "cuda" else torch.float32
)
#model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto")
qa_pipeline = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=250,
    pad_token_id=tokenizer.eos_token_id
)

# Set up LangChain
llm = HuggingFacePipeline(pipeline=qa_pipeline)
retriever = vectordb.as_retriever(search_kwargs={"k": 5})
qa_chain = RetrievalQA.from_chain_type(
    retriever=retriever,
    chain_type="stuff",
    llm=llm,
    return_source_documents=False
)

@spaces.GPU
def preprocess_query(query):
    if "script" in query or "code" in query.lower():
        return f"Write a CPSL script: {query}"
    return query

@spaces.GPU
def clean_response(response):
    result = response.get("result", "")
    if "Answer:" in result:
        return result.split("Answer:")[1].strip()
    return result.strip()

@spaces.GPU
def chatbot_response(user_input):
    processed_query = preprocess_query(user_input)
    raw_response = qa_chain.invoke({"query": processed_query})
    return clean_response(raw_response)

with gr.Blocks() as demo:  # Removed @spaces.GPU here
    gr.Markdown("# CPSL Chatbot")
    chat_history = gr.Chatbot()
    user_input = gr.Textbox(label="Your Message:")
    send_button = gr.Button("Send")

    @spaces.GPU
    def interact(user_message, history):
        bot_reply = chatbot_response(user_message)
        history.append((user_message, bot_reply))
        return history, history

    send_button.click(interact, inputs=[user_input, chat_history], outputs=[chat_history, chat_history])

demo.launch()