app.py

import os
from dotenv import load_dotenv
import gradio as gr
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_dotenv()

# Configure the Llama index settings
Settings.llm = HuggingFaceInferenceAPI(
    model_name="google/gemma-1.1-7b-it",
    tokenizer_name="google/gemma-1.1-7b-it",
    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 PDF directory exists
os.makedirs(PDF_DIRECTORY, exist_ok=True)
os.makedirs(PERSIST_DIR, exist_ok=True)

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 a RedfernsTech chatbot whose aim is to provide better service to the user, utilizing provided context to deliver answers.
            and collect the some basic information first also name, email, company name
            {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)

    query_engine = index.as_query_engine(text_qa_template=text_qa_template)
    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."

    # Append the query and response to chat history
    chat_history.append((query, response))

    return response

# Initialize chat history
chat_history = []

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

# Example query
query = "How do I use the RedfernsTech Q&A assistant?"
print("Query:", query)
response = handle_query(query)
print("Answer:", response)

# Define the input and output components for the Gradio interface
input_component = gr.Textbox(
    show_label=False,
    placeholder="Ask me anything about the document..."
)

output_component = gr.Textbox()

# Function to add chat history to output
def chat_with_history(query):
    response = handle_query(query)
    history_str = "\n\n".join([f"Query:\n{q}\nAnswer:\n{a}" for q, a in chat_history])
    return f"{response}\n\nChat History:\n\n{history_str}"

# Create the Gradio interface
interface = gr.Interface(
    fn=chat_with_history,
    inputs=input_component,
    outputs=output_component,
    title="RedfernsTech Q&A Chatbot",
    description="Ask me anything about the uploaded document. View chat history below."
)

# Launch the Gradio interface
interface.launch()