from dotenv import load_dotenv
import gradio as gr
import os
from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, Settings
from llama_index.llms.huggingface import HuggingFaceInferenceAPI
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from sentence_transformers import SentenceTransformer

# Load environment variables
load_dotenv()

# Configure the Llama index settings
Settings.llm = HuggingFaceInferenceAPI(
    model_name="meta-llama/Meta-Llama-3-8B-Instruct",
    tokenizer_name="meta-llama/Meta-Llama-3-8B-Instruct",
    context_window=3000,
    token=os.getenv("HF_TOKEN"),
    max_new_tokens=512,
    generate_kwargs={"temperature": 0.1},
)
Settings.embed_model = HuggingFaceEmbedding(
    model_name="BAAI/bge-small-en-v1.5"
)

# Define the directory for persistent storage and data
PERSIST_DIR = "db"
PDF_DIRECTORY = 'data'  # Changed to the directory containing PDFs

# Ensure directories exist
os.makedirs(PDF_DIRECTORY, exist_ok=True)
os.makedirs(PERSIST_DIR, exist_ok=True)

# Variable to store current chat conversation
current_chat_history = []

def data_ingestion_from_directory():
    # Use SimpleDirectoryReader on the directory containing the PDF files
    documents = SimpleDirectoryReader(PDF_DIRECTORY).load_data()
    storage_context = StorageContext.from_defaults()
    index = VectorStoreIndex.from_documents(documents)
    index.storage_context.persist(persist_dir=PERSIST_DIR)

def handle_query(query):
    chat_text_qa_msgs = [
        (
            "user",
            """
            You are now the RedFerns Tech chatbot. Your aim is to provide answers to the user based on the conversation flow only.
            {context_str}
            Question:
            {query_str}
            """
        )
    ]
    text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)

    # Load index from storage
    storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
    index = load_index_from_storage(storage_context)

    # Use chat history to enhance response
    context_str = ""
    for past_query, response in reversed(current_chat_history):
        if past_query.strip():
            context_str += f"User asked: '{past_query}'\nBot answered: '{response}'\n"

    query_engine = index.as_query_engine(text_qa_template=text_qa_template, context_str=context_str)
    answer = query_engine.query(query)

    if hasattr(answer, 'response'):
        response = answer.response
    elif isinstance(answer, dict) and 'response' in answer:
        response = answer['response']
    else:
        response = "Sorry, I couldn't find an answer."

    # Update current chat history
    current_chat_history.append((query, response))

    return response

# Example usage: Process PDF ingestion from directory
print("Processing PDF ingestion from directory:", PDF_DIRECTORY)
data_ingestion_from_directory()

# Define the function to handle predictions
def predict(message,history):
    response = handle_query(message)
    return response


# Create the chat interface with a custom layout function
css = '''
  /* Style the chat container */
  .gradio-container {
    display: flex;
    flex-direction: column;
    width: 450px;
    margin: 0 auto;
    padding: 20px;
    border: 1px solid #ddd;
    border-radius: 10px;
    background-color: #fff;
    box-shadow: 0 4px 8px rgba(0,0,0,0.1);
    position: relative;
    height: 600px; /* Adjust the height of the container */
  }

  /* Style the logo */
  .gradio-logo {
    display: flex;
    justify-content: center;
    margin-bottom: 20px;
  }

  .gradio-logo img {
    width: 100%;
    max-width: 300px;
  }

  /* Style the title */
  .gradio-title {
    text-align: center;
    font-weight: bold;
    font-size: 24px;
    margin-bottom: 20px;
    color: #4A90E2;
  }

  /* Style the chat history */
  .gradio-chat-history {
    flex: 1;
    overflow-y: auto;
    padding: 15px;
    border-bottom: 1px solid #ddd;
    background-color: #f9f9f9;
    border-radius: 5px;
    margin-bottom: 10px;
    max-height: 500px; /* Increase the height of the chat history */
  }

  /* Style the chat messages */
  .gradio-message {
    margin-bottom: 15px;
    display: flex;
    flex-direction: column; /* Stack messages vertically */
  }

  .gradio-message.user .gradio-message-content {
    background-color: #E1FFC7;
    align-self: flex-end;
    border: 1px solid #c3e6cb;
    border-radius: 15px 15px 0 15px;
    padding: 10px;
    font-size: 16px;
    margin-bottom: 5px;
    max-width: 80%;
  }

  .gradio-message.bot .gradio-message-content {
    background-color: #fff;
    align-self: flex-start;
    border: 1px solid #ced4da;
    border-radius: 15px 15px 15px 0;
    padding: 10px;
    font-size: 16px;
    margin-bottom: 5px;
    max-width: 80%;
  }

  .gradio-message-content {
    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
  }

  /* Style the footer */
  .gradio-footer {
    display: flex;
    padding: 10px;
    border-top: 1px solid #ddd;
    background-color: #F8D7DA; /* Light red background color */
    position: absolute;
    bottom: 0;
    width: calc(100% - 40px); /* Adjust width to match container padding */
  }

  /* Remove Gradio footer */
  footer {
    display: none !important;
    background-color: #F8D7DA;
  }
'''

# Create a custom HTML block for the logo
logo_html = '''
<div class="gradio-logo">
  <img src="https://redfernstech.com/wp-content/uploads/2024/05/RedfernsLogo_FinalV1.0-3-2048x575.png" alt="Company Logo">
</div>
'''

# Create a Blocks layout with the custom HTML and ChatInterface
with gr.Blocks(theme=gr.themes.Monochrome(), fill_height=True,css=css) as demo:
    gr.HTML(logo_html)
    gr.ChatInterface(predict)

# Launch the interface
demo.launch()