app.py

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. And tell user about our company services at beginning time 
            {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,clear_btn=None, 
                     undo_btn=None, 
                     retry_btn=None)

# Launch the interface
demo.launch()