app.py

import os
import logging
import re
import nltk
import spacy
import traceback
from nltk.tokenize import sent_tokenize
from langchain.vectorstores import Chroma
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_huggingface import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_groq import ChatGroq
from langchain.schema import Document
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
import chardet
import gradio as gr
import pandas as pd
import json

# Enable logging for debugging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)

# Set NLTK data path to the local 'nltk_data' directory
nltk.data.path.append(os.path.join(os.path.dirname(__file__), 'nltk_data'))
logger.debug("Configured NLTK data path to local 'nltk_data' directory.")

# Function to clean the API key
def clean_api_key(key):
    return ''.join(c for c in key if ord(c) < 128)

# Load the GROQ API key from environment variables (set as a secret in the Space)
api_key = os.getenv("GROQ_API_KEY")
if not api_key:
    logger.error("GROQ_API_KEY environment variable is not set. Please add it as a secret.")
    raise ValueError("GROQ_API_KEY environment variable is not set. Please add it as a secret.")
api_key = clean_api_key(api_key).strip()  # Clean and strip whitespace

# Function to clean text by removing non-ASCII characters
def clean_text(text):
    return text.encode("ascii", errors="ignore").decode()

# Function to load and clean documents from multiple file formats
def load_documents(file_paths):
    docs = []
    for file_path in file_paths:
        ext = os.path.splitext(file_path)[-1].lower()
        try:
            if ext == ".csv":
                # Handle CSV files
                with open(file_path, 'rb') as f:
                    result = chardet.detect(f.read())
                    encoding = result['encoding']
                data = pd.read_csv(file_path, encoding=encoding)
                for index, row in data.iterrows():
                    content = clean_text(row.to_string())
                    docs.append(Document(page_content=content, metadata={"source": file_path}))
            elif ext == ".json":
                # Handle JSON files
                with open(file_path, 'r', encoding='utf-8') as f:
                    data = json.load(f)
                    if isinstance(data, list):
                        for entry in data:
                            content = clean_text(json.dumps(entry))
                            docs.append(Document(page_content=content, metadata={"source": file_path}))
                    elif isinstance(data, dict):
                        content = clean_text(json.dumps(data))
                        docs.append(Document(page_content=content, metadata={"source": file_path}))
            elif ext == ".txt":
                # Handle TXT files
                with open(file_path, 'r', encoding='utf-8') as f:
                    content = clean_text(f.read())
                    docs.append(Document(page_content=content, metadata={"source": file_path}))
            else:
                logger.warning(f"Unsupported file format: {file_path}")
        except Exception as e:
            logger.error(f"Error processing file {file_path}: {e}")
            logger.error(traceback.format_exc())
    return docs

# Function to ensure the response ends with complete sentences using NLTK
def ensure_complete_sentences(text):
    logger.debug("Ensuring complete sentences for the given text.")
    try:
        sentences = sent_tokenize(text)
        if sentences:
            return ' '.join(sentences).strip()
        return text  # Return as is if no complete sentence is found
    except LookupError as e:
        logger.error("NLTK resource 'punkt' not found. Attempting to download again.")
        try:
            nltk.download('punkt', download_dir=os.path.join(os.path.dirname(__file__), 'nltk_data'))
            nltk.data.path.append(os.path.join(os.path.dirname(__file__), 'nltk_data'))
            sentences = sent_tokenize(text)
            return ' '.join(sentences).strip()
        except Exception as e_inner:
            logger.error("Failed to download 'punkt' resource.")
            logger.error(traceback.format_exc())
            raise e_inner
    except Exception as e:
        logger.error("Unexpected error during sentence tokenization.")
        logger.error(traceback.format_exc())
        raise e

# Advanced input validation using spaCy (Section 8a)
def is_valid_input_nlp(text, threshold=0.5):
    """
    Validates input text using spaCy's NLP capabilities.

    Parameters:
    - text (str): The input text to validate.
    - threshold (float): The minimum ratio of meaningful tokens required.

    Returns:
    - bool: True if the input is valid, False otherwise.
    """
    if not text or text.strip() == "":
        logger.debug("Input text is empty or contains only whitespace.")
        return False
    doc = nlp(text)
    meaningful_tokens = [token for token in doc if token.is_alpha]
    if not meaningful_tokens:
        logger.debug("No meaningful (alphabetic) tokens found in input.")
        return False
    ratio = len(meaningful_tokens) / len(doc)
    logger.debug(f"Meaningful tokens ratio: {ratio}")
    return ratio >= threshold

# Function to estimate prompt tokens (simple word count approximation)
def estimate_prompt_tokens(prompt):
    """
    Estimates the number of tokens in the prompt.
    This is a placeholder function. Replace it with actual token estimation logic.

    Parameters:
    - prompt (str): The prompt text.

    Returns:
    - int: Estimated number of tokens.
    """
    return len(prompt.split())

# Initialize the LLM using ChatGroq with GROQ's API
def initialize_llm(model, temperature, max_tokens, prompt_template):
    try:
        # Estimate prompt tokens
        estimated_prompt_tokens = estimate_prompt_tokens(prompt_template)
        logger.debug(f"Estimated prompt tokens: {estimated_prompt_tokens}")

        # Allocate remaining tokens to response
        response_max_tokens = max_tokens - estimated_prompt_tokens
        logger.debug(f"Response max tokens: {response_max_tokens}")

        if response_max_tokens <= 100:
            raise ValueError("max_tokens is too small to allocate for the response.")

        llm = ChatGroq(
            model=model,
            temperature=temperature,
            max_tokens=response_max_tokens,  # Adjusted max_tokens
            api_key=api_key  # Ensure the API key is passed correctly
        )
        logger.debug("LLM initialized successfully.")
        return llm
    except Exception as e:
        logger.error(f"Error initializing LLM: {e}")
        logger.error(traceback.format_exc())
        raise e

# Create the RAG pipeline
def create_rag_pipeline(file_paths, model, temperature, max_tokens):
    try:
        # Define the prompt template first to estimate tokens
        custom_prompt_template = PromptTemplate(
            input_variables=["context", "question"],
            template="""
            You are an AI assistant with expertise in daily wellness. Your aim is to provide detailed and comprehensive solutions regarding daily wellness topics without unnecessary verbosity.

            Context:
            {context}

            Question:
            {question}

            Provide a thorough and complete answer, including relevant examples and a suggested schedule. Ensure that the response does not end abruptly.
            """
        )

        # Estimate prompt tokens
        estimated_prompt_tokens = estimate_prompt_tokens(custom_prompt_template.template)
        logger.debug(f"Estimated prompt tokens from template: {estimated_prompt_tokens}")

        # Initialize the LLM with token allocation
        llm = initialize_llm(model, temperature, max_tokens, custom_prompt_template.template)

        # Load and process documents
        docs = load_documents(file_paths)
        if not docs:
            logger.warning("No documents were loaded. Please check your file paths and formats.")
            return None, "No documents were loaded. Please check your file paths and formats."

        # Split documents into chunks
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
        splits = text_splitter.split_documents(docs)
        logger.debug(f"Documents split into {len(splits)} chunks.")

        # Initialize the embedding model
        embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
        logger.debug("Embedding model initialized successfully.")

        # Use a temporary directory for Chroma vectorstore to prevent caching issues on Hugging Face Spaces
        vectorstore = Chroma.from_documents(
            documents=splits,
            embedding=embedding_model,
            persist_directory="/tmp/chroma_db"  # Temporary storage directory
        )
        vectorstore.persist()  # Save the database to disk
        logger.debug("Vectorstore initialized and persisted successfully.")

        retriever = vectorstore.as_retriever()

        # Create the RetrievalQA chain
        rag_chain = RetrievalQA.from_chain_type(
            llm=llm,
            chain_type="stuff",
            retriever=retriever,
            chain_type_kwargs={"prompt": custom_prompt_template}
        )
        logger.debug("RAG pipeline created successfully.")
        return rag_chain, "Pipeline created successfully."
    except Exception as e:
        logger.error(f"Error creating RAG pipeline: {e}")
        logger.error(traceback.format_exc())
        return None, f"Error creating RAG pipeline: {e}"

# Function to handle feedback (Section 8d)
def handle_feedback(feedback_text):
    """
    Handles user feedback by logging it.
    In a production environment, consider storing feedback in a database or external service.

    Parameters:
    - feedback_text (str): The feedback provided by the user.

    Returns:
    - str: Acknowledgment message.
    """
    if feedback_text and feedback_text.strip() != "":
        # For demonstration, we'll log the feedback. Replace this with database storage if needed.
        logger.info(f"User Feedback: {feedback_text}")
        return "Thank you for your feedback!"
    else:
        return "No feedback provided."

# Function to answer questions with input validation and post-processing
def answer_question(file_paths, model, temperature, max_tokens, question, feedback):
    try:
        # Validate input using spaCy-based validation
        if not is_valid_input_nlp(question):
            logger.debug("Invalid input detected.")
            return "Please provide a valid question or input containing meaningful text.", ""

        rag_chain, message = create_rag_pipeline(file_paths, model, temperature, max_tokens)
        if rag_chain is None:
            logger.debug("RAG pipeline creation failed.")
            return message, ""

        try:
            answer = rag_chain.run(question)
            logger.debug("Question answered successfully.")
            # Post-process to ensure the answer ends with complete sentences
            complete_answer = ensure_complete_sentences(answer)

            # Handle feedback
            feedback_response = handle_feedback(feedback)

            return complete_answer, feedback_response
        except Exception as e_inner:
            logger.error(f"Error during RAG pipeline execution: {e_inner}")
            logger.error(traceback.format_exc())
            return f"Error during RAG pipeline execution: {e_inner}", ""

    except Exception as e_outer:
        logger.error(f"Unexpected error in answer_question: {e_outer}")
        logger.error(traceback.format_exc())
        return f"Unexpected error: {e_outer}", ""

# Gradio Interface with Feedback Mechanism (Section 8d)
def gradio_interface(model, temperature, max_tokens, question, feedback):
    file_paths = ['AIChatbot.csv']  # Ensure this file is present in your Space root directory
    return answer_question(file_paths, model, temperature, max_tokens, question, feedback)

# Define Gradio UI
interface = gr.Interface(
    fn=gradio_interface,
    inputs=[
        gr.Textbox(
            label="Model Name",
            value="llama3-8b-8192",
            placeholder="e.g., llama3-8b-8192"
        ),
        gr.Slider(
            label="Temperature",
            minimum=0,
            maximum=1,
            step=0.01,
            value=0.7,
            info="Controls the randomness of the response. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic."
        ),
        gr.Slider(
            label="Max Tokens",
            minimum=200,
            maximum=2048,
            step=1,
            value=500,
            info="Determines the maximum number of tokens in the response. Higher values allow for longer answers."
        ),
        gr.Textbox(
            label="Question",
            placeholder="e.g., What is box breathing and how does it help reduce anxiety?"
        ),
        gr.Textbox(
            label="Feedback",
            placeholder="Provide your feedback here...",
            lines=2
        )
    ],
    outputs=[
        "text",
        "text"
    ],
    title="Daily Wellness AI",
    description="Ask questions about daily wellness and get detailed solutions.",
    examples=[
        ["llama3-8b-8192", 0.7, 500, "What is box breathing and how does it help reduce anxiety?", "Great explanation!"],
        ["llama3-8b-8192", 0.6, 600, "Provide a daily wellness schedule incorporating box breathing techniques.", "Very helpful, thank you!"]
    ],
    allow_flagging="never"  # Disable default flagging; using custom feedback
)

# Launch Gradio app without share=True (not supported on Hugging Face Spaces)
if __name__ == "__main__":
    interface.launch(server_name="0.0.0.0", server_port=7860, debug=True)