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RAG_application_PDF_test1

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import os
import gradio as gr
from groq import Groq
import torch # For checking CUDA availability for embedding model
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyPDFLoader # For PDF loading
from langchain_community.embeddings import HuggingFaceEmbeddings # For open-source embeddings
from langchain_community.vectorstores import FAISS # For vector database
# Removed: from google.colab import userdata # This library is specific to Google Colab
import gc # For garbage collection, useful in Colab/Spaces

# --- Configuration & Global Variables ---
# IMPORTANT: Ensure your GROQ_API_KEY is set in Hugging Face Space's Repository Secrets!
# It will be directly available via os.environ.get()

# Groq LLM Model
GROQ_MODEL = "llama-3.3-70b-versatile" # A fast and capable open-source model available via Groq

# Embedding Model (Open-source, free, and efficient)
# Model: 'sentence-transformers/all-MiniLM-L6-v2' is a good balance of size and performance.
EMBEDDING_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
EMBEDDING_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

# Global state for Groq client, embedding model, and FAISS vector store
groq_client = None
embedding_model = None
# This will hold the FAISS vector store after a PDF is uploaded and processed
# It's initialized to None and will be updated via Gradio's State.
faiss_vector_store = None
llm_chat_history = [] # For maintaining conversational context with Groq

# --- Initialization Functions ---
def initialize_groq_client():
    """Initializes the Groq client from environment variable."""
    global groq_client
    try:
        # Directly get the API key from environment variables.
        # This works automatically when you set secrets in Hugging Face Spaces.
        groq_api_key = os.environ.get("GROK_API_KEY")

        if not groq_api_key:
            raise ValueError("GROQ_API_KEY environment variable is not set. Please add it to your Hugging Face Space's Repository Secrets.")
        groq_client = Groq(api_key=groq_api_key)
        print("Groq client initialized successfully.")
    except ValueError as ve:
        print(f"ERROR: Groq client initialization failed: {ve}")
        print("ACTION REQUIRED: Ensure 'GROQ_API_KEY' is set correctly in your Hugging Face Space's Repository Secrets.")
        groq_client = None
    except Exception as e:
        print(f"ERROR: An unexpected error occurred during Groq client initialization: {e}")
        groq_client = None

def initialize_embedding_model():
    """Initializes the HuggingFace embedding model."""
    global embedding_model
    try:
        print(f"Loading embedding model: {EMBEDDING_MODEL_NAME} on {EMBEDDING_DEVICE}...")
        embedding_model = HuggingFaceEmbeddings(
            model_name=EMBEDDING_MODEL_NAME,
            model_kwargs={'device': EMBEDDING_DEVICE},
            encode_kwargs={'normalize_embeddings': True} # Recommended for cosine similarity
        )
        print("Embedding model initialized successfully.")
    except Exception as e:
        print(f"ERROR: Embedding model initialization failed: {e}")
        print("ACTION REQUIRED: Check network connection and ensure 'transformers' library dependencies are met. Consider Space GPU availability if using 'cuda'.")
        embedding_model = None

# Initialize clients/models once when the app starts
initialize_groq_client()
initialize_embedding_model()

# --- PDF Processing and FAISS Indexing Function ---
def process_pdf_and_create_index(pdf_file: gr.File):
    """
    Loads text from a PDF, chunks it, creates embeddings,
    and builds a FAISS vector store.
    Args:
        pdf_file (gr.File): The Gradio File object containing the uploaded PDF.
    Returns:
        tuple: (status_message, FAISS_vector_store_object)
    """
    global faiss_vector_store
    global llm_chat_history # Clear chat history on new PDF upload

    if embedding_model is None:
        return "Error: Embedding model not loaded. Cannot process PDF.", None

    if pdf_file is None:
        return "Please upload a PDF file to process.", None

    file_path = pdf_file.name # Get the temporary file path from Gradio
    print(f"Processing PDF: {file_path}")

    try:
        # 1. Load PDF Document
        loader = PyPDFLoader(file_path)
        documents = loader.load()
        print(f"Loaded {len(documents)} pages from PDF.")

        # 2. Split Text into Chunks
        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=1000,
            chunk_overlap=200,
            length_function=len,
            add_start_index=True,
        )
        chunks = text_splitter.split_documents(documents)
        print(f"Split document into {len(chunks)} chunks.")

        # 3. Create Embeddings and Build FAISS Index
        print("Creating embeddings and building FAISS index... This may take a while.")
        faiss_vector_store = FAISS.from_documents(chunks, embedding_model)
        print("FAISS index created successfully!")

        # Clear existing chat history for new document context
        llm_chat_history.clear()
        gc.collect() # Clean up memory

        return "PDF processed successfully! You can now start chatting.", faiss_vector_store

    except Exception as e:
        print(f"ERROR during PDF processing: {e}")
        faiss_vector_store = None # Reset store on error
        return f"Error processing PDF: {e}. Please ensure it's a valid PDF.", None

# --- Chat Function with RAG ---
def chat_with_rag(user_query: str, chat_history: list, current_vector_store: FAISS):
    """
    Generates a response using Groq, augmented by context retrieved from the FAISS vector store.
    Args:
        user_query (str): The current message from the user.
        chat_history (list): Gradio's chat history (list of [user_text, bot_text] tuples).
        current_vector_store (FAISS): The loaded FAISS index containing document embeddings.
    Returns:
        tuple: (updated_gradio_chat_history, bot_response_text)
    """
    global llm_chat_history # Access the global LLM context history

    if current_vector_store is None:
        bot_response = "Please upload and process a PDF document first before asking questions."
        llm_chat_history.append({"role": "user", "content": user_query})
        llm_chat_history.append({"role": "assistant", "content": bot_response})
        # Gradio's Chatbot handles updating the display history from this.
        return chat_history + [[user_query, bot_response]], "" # Return updated Gradio history and empty text input

    if groq_client is None:
        bot_response = "Groq client not initialized. Cannot generate response. Check API key setup."
        llm_chat_history.append({"role": "user", "content": user_query})
        llm_chat_history.append({"role": "assistant", "content": bot_response})
        return chat_history + [[user_query, bot_response]], ""

    print(f"User Query: {user_query}")

    try:
        # 1. Retrieve relevant documents from FAISS
        # Adjust k (number of results) based on how much context you need
        retrieved_docs = current_vector_store.similarity_search(user_query, k=4)
        context_text = "\n\n".join([doc.page_content for doc in retrieved_docs])
        print(f"Retrieved Context:\n{context_text[:500]}...") # Print first 500 chars

        # 2. Augment the user query with retrieved context for the LLM
        # Ensure Groq LLM understands the context's role
        augmented_query = (
            f"Based on the following context, answer the question. "
            f"If the answer is not in the context, state that you don't have enough information.\n\n"
            f"Context:\n{context_text}\n\n"
            f"Question: {user_query}"
        )

        # 3. Prepare messages for Groq API (including chat history)
        # We need to build the 'messages' list for Groq, including conversation history.
        # Gradio's chat_history is [[user, bot], [user, bot], ...]
        groq_messages = []
        for human_msg, ai_msg in chat_history:
            # Only add to Groq's history if not empty
            if human_msg:
                groq_messages.append({"role": "user", "content": human_msg})
            if ai_msg:
                groq_messages.append({"role": "assistant", "content": ai_msg})
        
        # Add the current augmented query as the latest user message
        groq_messages.append({"role": "user", "content": augmented_query})

        # 4. Generate response using Groq
        chat_completion = groq_client.chat.completions.create(
            messages=groq_messages,
            model=GROQ_MODEL,
            temperature=0.7,
            max_tokens=1024,
            top_p=1,
            stop=None,
            stream=False,
        )
        bot_response = chat_completion.choices[0].message.content
        print(f"Groq Response: {bot_response}")

        # Update global LLM chat history for next turn's context
        llm_chat_history.append({"role": "user", "content": user_query}) # Store original query
        llm_chat_history.append({"role": "assistant", "content": bot_response})

        # Return the updated Gradio chat history and clear the text input
        return chat_history + [[user_query, bot_response]], ""

    except Exception as e:
        error_message = f"An error occurred during RAG process: {e}. Please try again."
        print(f"RAG Chat Error: {e}")
        llm_chat_history.append({"role": "user", "content": user_query})
        llm_chat_history.append({"role": "assistant", "content": error_message})
        return chat_history + [[user_query, error_message]], ""

# --- Gradio Interface ---
with gr.Blocks(theme=gr.themes.Soft(), title="RAG PDF Chatbot") as demo:
    gr.Markdown(
        """
        # 📚 Sheikh's -- You can Chat with your PDF (RAG Application) 💬
        Upload a PDF document, wait for it to process, and then ask questions about its content!
        Powered by open-source models.
        """
    )

    # State to store the FAISS vector index (in-memory)
    # This state will persist the index across chat turns within a session.
    # It's initialized to None and updated by the PDF processing function.
    vector_store_state = gr.State(faiss_vector_store)

    with gr.Row():
        with gr.Column(scale=1):
            pdf_upload_input = gr.File(
                label="Upload your PDF Document and free IK",
                file_types=[".pdf"],
                file_count="single"
            )
            process_pdf_btn = gr.Button("Process PDF 🚀")
            pdf_process_status = gr.Textbox(
                label="PDF Processing Status",
                interactive=False,
                lines=1
            )
            # Add a progress bar (useful for longer PDFs)
            # You can connect a progress event to this if needed, for now just a placeholder
            # progress_bar = gr.Progress(label="Processing Progress")

        with gr.Column(scale=2):
            chatbot = gr.Chatbot(
                label="Conversation History",
                value=[],
                height=400,
                show_copy_button=True
            )
            text_input = gr.Textbox(
                label="Type your question here",
                placeholder="Ask me about the PDF content...",
                lines=3
            )
            with gr.Row():
                submit_btn = gr.Button("Send Message ➡️")
                clear_chat_btn = gr.Button("Clear Chat 🗑️")

    # --- Event Handlers ---
    # 1. When PDF is uploaded and "Process PDF" button is clicked
    process_pdf_btn.click(
        fn=process_pdf_and_create_index,
        inputs=[pdf_upload_input],
        outputs=[pdf_process_status, vector_store_state], # Update status and the state variable
        # Add a loading indicator to the button itself
        api_name="process_pdf"
    )

    # 2. When text is entered and "Send Message" button is clicked
    submit_btn.click(
        fn=chat_with_rag,
        inputs=[text_input, chatbot, vector_store_state], # Pass query, current chat history, and the vector store state
        outputs=[chatbot, text_input], # Update chat history and clear text input
        api_name="send_message_button"
    )

    # 3. When text is entered and Enter key is pressed
    text_input.submit(
        fn=chat_with_rag,
        inputs=[text_input, chatbot, vector_store_state], # Pass query, current chat history, and the vector store state
        outputs=[chatbot, text_input], # Update chat history and clear text input
        api_name="send_message_enter"
    )

    # 4. Clear chat button functionality
    clear_chat_btn.click(
        fn=lambda: ([], ""), # Clear chatbot display and text input box
        inputs=[],
        outputs=[chatbot, text_input],
        queue=False
    ).success(
        fn=lambda: llm_chat_history.clear(), # Clear the global LLM history list
        inputs=[],
        outputs=[]
    )

# Launch the Gradio app
if __name__ == "__main__":
    # For Hugging Face Spaces deployment, `share=True` is not needed as it's automatically public.
    # The default demo.launch() will work.
    demo.launch()

	import os
	import gradio as gr
	from groq import Groq
	import torch # For checking CUDA availability for embedding model
	from langchain.text_splitter import RecursiveCharacterTextSplitter
	from langchain_community.document_loaders import PyPDFLoader # For PDF loading
	from langchain_community.embeddings import HuggingFaceEmbeddings # For open-source embeddings
	from langchain_community.vectorstores import FAISS # For vector database
	# Removed: from google.colab import userdata # This library is specific to Google Colab
	import gc # For garbage collection, useful in Colab/Spaces

	# --- Configuration & Global Variables ---
	# IMPORTANT: Ensure your GROQ_API_KEY is set in Hugging Face Space's Repository Secrets!
	# It will be directly available via os.environ.get()

	# Groq LLM Model
	GROQ_MODEL = "llama-3.3-70b-versatile" # A fast and capable open-source model available via Groq

	# Embedding Model (Open-source, free, and efficient)
	# Model: 'sentence-transformers/all-MiniLM-L6-v2' is a good balance of size and performance.
	EMBEDDING_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
	EMBEDDING_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

	# Global state for Groq client, embedding model, and FAISS vector store
	groq_client = None
	embedding_model = None
	# This will hold the FAISS vector store after a PDF is uploaded and processed
	# It's initialized to None and will be updated via Gradio's State.
	faiss_vector_store = None
	llm_chat_history = [] # For maintaining conversational context with Groq

	# --- Initialization Functions ---
	def initialize_groq_client():
	"""Initializes the Groq client from environment variable."""
	global groq_client
	try:
	# Directly get the API key from environment variables.
	# This works automatically when you set secrets in Hugging Face Spaces.
	groq_api_key = os.environ.get("GROK_API_KEY")

	if not groq_api_key:
	raise ValueError("GROQ_API_KEY environment variable is not set. Please add it to your Hugging Face Space's Repository Secrets.")
	groq_client = Groq(api_key=groq_api_key)
	print("Groq client initialized successfully.")
	except ValueError as ve:
	print(f"ERROR: Groq client initialization failed: {ve}")
	print("ACTION REQUIRED: Ensure 'GROQ_API_KEY' is set correctly in your Hugging Face Space's Repository Secrets.")
	groq_client = None
	except Exception as e:
	print(f"ERROR: An unexpected error occurred during Groq client initialization: {e}")
	groq_client = None

	def initialize_embedding_model():
	"""Initializes the HuggingFace embedding model."""
	global embedding_model
	try:
	print(f"Loading embedding model: {EMBEDDING_MODEL_NAME} on {EMBEDDING_DEVICE}...")
	embedding_model = HuggingFaceEmbeddings(
	model_name=EMBEDDING_MODEL_NAME,
	model_kwargs={'device': EMBEDDING_DEVICE},
	encode_kwargs={'normalize_embeddings': True} # Recommended for cosine similarity
	)
	print("Embedding model initialized successfully.")
	except Exception as e:
	print(f"ERROR: Embedding model initialization failed: {e}")
	print("ACTION REQUIRED: Check network connection and ensure 'transformers' library dependencies are met. Consider Space GPU availability if using 'cuda'.")
	embedding_model = None

	# Initialize clients/models once when the app starts
	initialize_groq_client()
	initialize_embedding_model()

	# --- PDF Processing and FAISS Indexing Function ---
	def process_pdf_and_create_index(pdf_file: gr.File):
	"""
	Loads text from a PDF, chunks it, creates embeddings,
	and builds a FAISS vector store.

	Args:
	pdf_file (gr.File): The Gradio File object containing the uploaded PDF.

	Returns:
	tuple: (status_message, FAISS_vector_store_object)
	"""
	global faiss_vector_store
	global llm_chat_history # Clear chat history on new PDF upload

	if embedding_model is None:
	return "Error: Embedding model not loaded. Cannot process PDF.", None

	if pdf_file is None:
	return "Please upload a PDF file to process.", None

	file_path = pdf_file.name # Get the temporary file path from Gradio
	print(f"Processing PDF: {file_path}")

	try:
	# 1. Load PDF Document
	loader = PyPDFLoader(file_path)
	documents = loader.load()
	print(f"Loaded {len(documents)} pages from PDF.")

	# 2. Split Text into Chunks
	text_splitter = RecursiveCharacterTextSplitter(
	chunk_size=1000,
	chunk_overlap=200,
	length_function=len,
	add_start_index=True,
	)
	chunks = text_splitter.split_documents(documents)
	print(f"Split document into {len(chunks)} chunks.")

	# 3. Create Embeddings and Build FAISS Index
	print("Creating embeddings and building FAISS index... This may take a while.")
	faiss_vector_store = FAISS.from_documents(chunks, embedding_model)
	print("FAISS index created successfully!")

	# Clear existing chat history for new document context
	llm_chat_history.clear()
	gc.collect() # Clean up memory

	return "PDF processed successfully! You can now start chatting.", faiss_vector_store

	except Exception as e:
	print(f"ERROR during PDF processing: {e}")
	faiss_vector_store = None # Reset store on error
	return f"Error processing PDF: {e}. Please ensure it's a valid PDF.", None

	# --- Chat Function with RAG ---
	def chat_with_rag(user_query: str, chat_history: list, current_vector_store: FAISS):
	"""
	Generates a response using Groq, augmented by context retrieved from the FAISS vector store.

	Args:
	user_query (str): The current message from the user.
	chat_history (list): Gradio's chat history (list of [user_text, bot_text] tuples).
	current_vector_store (FAISS): The loaded FAISS index containing document embeddings.

	Returns:
	tuple: (updated_gradio_chat_history, bot_response_text)
	"""
	global llm_chat_history # Access the global LLM context history

	if current_vector_store is None:
	bot_response = "Please upload and process a PDF document first before asking questions."
	llm_chat_history.append({"role": "user", "content": user_query})
	llm_chat_history.append({"role": "assistant", "content": bot_response})
	# Gradio's Chatbot handles updating the display history from this.
	return chat_history + [[user_query, bot_response]], "" # Return updated Gradio history and empty text input

	if groq_client is None:
	bot_response = "Groq client not initialized. Cannot generate response. Check API key setup."
	llm_chat_history.append({"role": "user", "content": user_query})
	llm_chat_history.append({"role": "assistant", "content": bot_response})
	return chat_history + [[user_query, bot_response]], ""

	print(f"User Query: {user_query}")

	try:
	# 1. Retrieve relevant documents from FAISS
	# Adjust k (number of results) based on how much context you need
	retrieved_docs = current_vector_store.similarity_search(user_query, k=4)
	context_text = "\n\n".join([doc.page_content for doc in retrieved_docs])
	print(f"Retrieved Context:\n{context_text[:500]}...") # Print first 500 chars

	# 2. Augment the user query with retrieved context for the LLM
	# Ensure Groq LLM understands the context's role
	augmented_query = (
	f"Based on the following context, answer the question. "
	f"If the answer is not in the context, state that you don't have enough information.\n\n"
	f"Context:\n{context_text}\n\n"
	f"Question: {user_query}"
	)

	# 3. Prepare messages for Groq API (including chat history)
	# We need to build the 'messages' list for Groq, including conversation history.
	# Gradio's chat_history is [[user, bot], [user, bot], ...]
	groq_messages = []
	for human_msg, ai_msg in chat_history:
	# Only add to Groq's history if not empty
	if human_msg:
	groq_messages.append({"role": "user", "content": human_msg})
	if ai_msg:
	groq_messages.append({"role": "assistant", "content": ai_msg})

	# Add the current augmented query as the latest user message
	groq_messages.append({"role": "user", "content": augmented_query})

	# 4. Generate response using Groq
	chat_completion = groq_client.chat.completions.create(
	messages=groq_messages,
	model=GROQ_MODEL,
	temperature=0.7,
	max_tokens=1024,
	top_p=1,
	stop=None,
	stream=False,
	)
	bot_response = chat_completion.choices[0].message.content
	print(f"Groq Response: {bot_response}")

	# Update global LLM chat history for next turn's context
	llm_chat_history.append({"role": "user", "content": user_query}) # Store original query
	llm_chat_history.append({"role": "assistant", "content": bot_response})

	# Return the updated Gradio chat history and clear the text input
	return chat_history + [[user_query, bot_response]], ""

	except Exception as e:
	error_message = f"An error occurred during RAG process: {e}. Please try again."
	print(f"RAG Chat Error: {e}")
	llm_chat_history.append({"role": "user", "content": user_query})
	llm_chat_history.append({"role": "assistant", "content": error_message})
	return chat_history + [[user_query, error_message]], ""

	# --- Gradio Interface ---
	with gr.Blocks(theme=gr.themes.Soft(), title="RAG PDF Chatbot") as demo:
	gr.Markdown(
	"""
	# 📚 Sheikh's -- You can Chat with your PDF (RAG Application) 💬
	Upload a PDF document, wait for it to process, and then ask questions about its content!
	Powered by open-source models.
	"""
	)

	# State to store the FAISS vector index (in-memory)
	# This state will persist the index across chat turns within a session.
	# It's initialized to None and updated by the PDF processing function.
	vector_store_state = gr.State(faiss_vector_store)

	with gr.Row():
	with gr.Column(scale=1):
	pdf_upload_input = gr.File(
	label="Upload your PDF Document and free IK",
	file_types=[".pdf"],
	file_count="single"
	)
	process_pdf_btn = gr.Button("Process PDF 🚀")
	pdf_process_status = gr.Textbox(
	label="PDF Processing Status",
	interactive=False,
	lines=1
	)
	# Add a progress bar (useful for longer PDFs)
	# You can connect a progress event to this if needed, for now just a placeholder
	# progress_bar = gr.Progress(label="Processing Progress")

	with gr.Column(scale=2):
	chatbot = gr.Chatbot(
	label="Conversation History",
	value=[],
	height=400,
	show_copy_button=True
	)
	text_input = gr.Textbox(
	label="Type your question here",
	placeholder="Ask me about the PDF content...",
	lines=3
	)
	with gr.Row():
	submit_btn = gr.Button("Send Message ➡️")
	clear_chat_btn = gr.Button("Clear Chat 🗑️")

	# --- Event Handlers ---
	# 1. When PDF is uploaded and "Process PDF" button is clicked
	process_pdf_btn.click(
	fn=process_pdf_and_create_index,
	inputs=[pdf_upload_input],
	outputs=[pdf_process_status, vector_store_state], # Update status and the state variable
	# Add a loading indicator to the button itself
	api_name="process_pdf"
	)

	# 2. When text is entered and "Send Message" button is clicked
	submit_btn.click(
	fn=chat_with_rag,
	inputs=[text_input, chatbot, vector_store_state], # Pass query, current chat history, and the vector store state
	outputs=[chatbot, text_input], # Update chat history and clear text input
	api_name="send_message_button"
	)

	# 3. When text is entered and Enter key is pressed
	text_input.submit(
	fn=chat_with_rag,
	inputs=[text_input, chatbot, vector_store_state], # Pass query, current chat history, and the vector store state
	outputs=[chatbot, text_input], # Update chat history and clear text input
	api_name="send_message_enter"
	)

	# 4. Clear chat button functionality
	clear_chat_btn.click(
	fn=lambda: ([], ""), # Clear chatbot display and text input box
	inputs=[],
	outputs=[chatbot, text_input],
	queue=False
	).success(
	fn=lambda: llm_chat_history.clear(), # Clear the global LLM history list
	inputs=[],
	outputs=[]
	)

	# Launch the Gradio app
	if __name__ == "__main__":
	# For Hugging Face Spaces deployment, `share=True` is not needed as it's automatically public.
	# The default demo.launch() will work.
	demo.launch()