below i will give you two code code 1 & code 2.
import gradio as gr
from typing import Any, Optional, List
from PIL import Image
import io
import pytesseract
import fitz  # PyMuPDF
import cv2
import torch
import torchvision
from torchvision import transforms
import os

from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain.llms.base import LLM
import google.generativeai as genai
from langchain_community.llms import HuggingFaceEndpoint  # For Hugging Face LLMs

# Initialize global variables
llm = None
vectorstore = None  # For file data
file_text = ""  # Store extracted text from file

###########################
# File Processing Section #
###########################

def extract_text_from_pdf(pdf_path):
    """Extracts text from both textual content and images within a PDF using PyMuPDF."""
    text = ""
    doc = fitz.open(pdf_path)

    for page_num in range(doc.page_count):
        page = doc.load_page(page_num)
        text += page.get_text("text")  # Extract text from the page
        images = page.get_images(full=True)
        for img in images:
            xref = img[0]
            try:
                base_image = doc.extract_image(xref)
                image_data = base_image["image"]
                if isinstance(image_data, bytes):
                    image = Image.open(io.BytesIO(image_data))
                    text += pytesseract.image_to_string(image)
            except Exception as e:
                print(f"Error extracting image on page {page_num}: {e}")

    return text

def analyze_image(image_path):
    """Analyzes an image, extracting text and detecting objects."""
    text = ""
    try:
        image = Image.open(image_path)
        text += pytesseract.image_to_string(image)
    except Exception as e:
        print(f"Error during OCR: {e}")

    try:
        img = cv2.imread(image_path)
        transform = transforms.Compose([transforms.ToTensor()])
        img_tensor = transform(img).unsqueeze(0)

        model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
        model.eval()

        with torch.no_grad():
            predictions = model(img_tensor)

        for element in predictions[0]['labels']:
            text += f"Detected object with label: {element.item()}\n"
    except Exception as e:
        print(f"Error during object detection: {e}")

    return text

def process_files(files):
    """Processes uploaded files, extracting text from PDFs and images."""
    global file_text  # Access the global file_text variable
    text_chunks = []

    if files:
        for file in files:
            if file.name.endswith(".pdf"):
                try:
                    text = extract_text_from_pdf(file.name)
                    text_splitter = RecursiveCharacterTextSplitter(
                        chunk_size=1000,
                        chunk_overlap=200,
                    )
                    chunks = text_splitter.split_text(text)
                    text_chunks.extend(chunks)
                    file_text = text  # Store the entire text in file_text
                except Exception as e:
                    return "Error", f"Error processing PDF {file.name}: {str(e)}"
            elif file.name.endswith((".png", ".jpg", ".jpeg")):
                try:
                    text = analyze_image(file.name)
                    text_chunks.append(text)
                    file_text += text  # Append the image text to file_text
                except Exception as e:
                    return (
                        "Error",
                        f"Error processing image {file.name}: {str(e)}",
                    )

    if text_chunks:
        return text_chunks
    else:
        return "Error", "No files were successfully processed."

###########################
# LLM Wrapper Section     #
###########################

class GeminiWrapper(LLM):
    """Wrapper around Google Gemini LLMs for use with LangChain."""

    model: Any

    def __init__(self, api_key: str, model_name: str):
        """Initializes the GeminiWrapper with API key and model name."""
        super().__init__()
        genai.configure(api_key=api_key)
        self.model = genai.GenerativeModel(model_name)

    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
        """Calls the Gemini model to generate text from the given prompt."""
        response = self.model.generate_content(prompt)
        return response.text

    @property
    def _llm_type(self) -> str:
        """Returns the type of LLM ('gemini')."""
        return "gemini"

# List of supported LLMs 
list_llm = [
    "Google Gemini 1.5 Pro",
    "meta-llama/Meta-Llama-3-70B-Instruct", 
    "meta-llama/Meta-Llama-3-70B",
    "meta-llama/Meta-Llama-3-8B-Instruct", 
    "google/gemma-2-27b", 
    "microsoft/Phi-3-medium-128k-instruct",
    "microsoft/Phi-3-vision-128k-instruct",
    "mistralai/Mixtral-8x22B-Instruct-v0.1",
    "Qwen/Qwen2-72B",
    "Qwen/Qwen2-72B-Instruct",
    "llava-hf/llava-v1.6-34b-hf",
    "tiiuae/falcon-40b-instruct",
    "CohereForAI/c4ai-command-r-plus" 
]
list_llm_simple = [os.path.basename(llm) if "/" in llm else llm for llm in list_llm] 

def initialize_llm(model_choice, api_key, hf_token):  # Take API keys as arguments
    """Initializes the selected LLM based on user choice and API keys."""
    if model_choice == "Google Gemini 1.5 Pro":
        return GeminiWrapper(api_key, "gemini-pro") 
    else:
        # All other LLMs use HuggingFaceEndpoint
        try:
            # Model-specific parameters (adjust as needed)
            model_kwargs = {
                "temperature": 0.7,
                "max_new_tokens": 1024,
                "top_k": 3,
            }

            # Add 'trust_remote_code=True' where needed
            if "microsoft/phi" in model_choice or "tiiuae/falcon" in model_choice:
                model_kwargs["trust_remote_code"] = True

            llm = HuggingFaceEndpoint(
                repo_id=model_choice, 
                task="text-generation", # Explicitly set the task
                token=hf_token,  # Pass the Hugging Face token here
                **model_kwargs 
            )
            return llm
        except Exception as e:
            return f"LLM initialization failed: {str(e)}" 

###########################
# Chat Functions Section  #
###########################

def chat(message, history, llm, vectorstore=None, file_text=""):
    """Handles chatbot interactions, prioritizing file context when relevant."""
    if llm is None:
        return history, "Please initialize the chatbot first."

    response_from_files = ""
    if vectorstore is not None:
        memory = ConversationBufferMemory(memory_key="chat_history")
        qa_chain = ConversationalRetrievalChain.from_llm(
            llm, retriever=vectorstore.as_retriever(), memory=memory
        )
        response_from_files = qa_chain({"question": message})["answer"]

        # Check if the response from files is relevant and not a fallback 
        if "I do not have enough information to answer your question" not in response_from_files:
            # Prioritize file-based response
            response = response_from_files
        else:
            # Fallback to LLM's knowledge
            response = llm._call(message)
    else:
        # No files, rely solely on LLM
        response = llm._call(message)

    history.append((message, response))
    return history, ""

def initialize_chatbot(model_choice, api_key, hf_token, files): # Pass the tokens here
    """Initializes the chatbot with the selected LLM and file data."""
    global llm, vectorstore

    llm = initialize_llm(model_choice, api_key, hf_token) # Pass the tokens to initialize_llm
    if isinstance(llm, str):
        return f"LLM initialization failed: {llm}"

    if files:
        text_chunks = process_files(files)
        if text_chunks[0] == "Error":
            return f"File processing failed: {text_chunks[1]}"

        from langchain.schema import Document

        text_chunks = [Document(page_content=text) for text in text_chunks]

        vectorstore = FAISS.from_documents(text_chunks, HuggingFaceEmbeddings())
        return "Chatbot initialized with file context!"
    else:
        vectorstore = None
        return "Chatbot initialized! (No file context)"

###########################
# Gradio Interface Section#
###########################

with gr.Blocks() as demo:
    gr.Markdown("# AI-Powered Chatbot with File Enhancement")

    with gr.Row():
        model_choice = gr.Dropdown(
            list_llm_simple, 
            label="Select LLM",
        )
        
        # Add textboxes for API keys
        api_key_input = gr.Textbox(label="Google Gemini API Key", type="password")
        hf_token_input = gr.Textbox(label="Hugging Face Token", type="password")

    gr.Markdown("### Upload Your Files")
    file_upload = gr.Files(
        label="", 
        file_count="multiple",
        file_types=["pdf", "png", "jpg", "jpeg"],
    )

    initialize_btn = gr.Button("Initialize Chatbot")
    status = gr.Textbox(label="Status")

    chatbot = gr.Chatbot()
    with gr.Row():
        msg = gr.Textbox(show_label=False, placeholder="Enter text and press enter")
        send_btn = gr.Button("Send", scale=0)
        clear_btn = gr.Button("Clear", scale=0)

    initialize_btn.click(
        initialize_chatbot,
        inputs=[model_choice, api_key_input, hf_token_input, file_upload], # Pass the API keys 
        outputs=[status],
    )

    def send_message(message, chat_history):
        global llm, vectorstore, file_text  # Access global variables
        return chat(message, chat_history, llm, vectorstore, file_text=file_text) 

    def clear_chat():
        return None

    send_btn.click(
        send_message, 
        inputs=[msg, chatbot], 
        outputs=[chatbot, msg]
    )
    clear_btn.click(clear_chat, outputs=[chatbot])
    msg.submit(
        send_message, 
        inputs=[msg, chatbot], 
        outputs=[chatbot, msg]
    )

demo.launch()


code 2: import gradio as gr
import os

from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain
from langchain_community.embeddings import HuggingFaceEmbeddings 
from langchain_community.llms import HuggingFacePipeline
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain_community.llms import HuggingFaceEndpoint

from pathlib import Path
import chromadb
from unidecode import unidecode

from transformers import AutoTokenizer
import transformers
import torch
import tqdm 
import accelerate
import re



# default_persist_directory = './chroma_HF/'
list_llm = ["mistralai/Mistral-7B-Instruct-v0.2", "mistralai/Mixtral-8x7B-Instruct-v0.1", "mistralai/Mistral-7B-Instruct-v0.1", \
    "google/gemma-7b-it","google/gemma-2b-it", \
    "HuggingFaceH4/zephyr-7b-beta", "HuggingFaceH4/zephyr-7b-gemma-v0.1", \
    "meta-llama/Llama-2-7b-chat-hf", "microsoft/phi-2", \
    "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "mosaicml/mpt-7b-instruct", "tiiuae/falcon-7b-instruct", \
    "google/flan-t5-xxl"
]
list_llm_simple = [os.path.basename(llm) for llm in list_llm]

# Load PDF document and create doc splits
def load_doc(list_file_path, chunk_size, chunk_overlap):
    # Processing for one document only
    # loader = PyPDFLoader(file_path)
    # pages = loader.load()
    loaders = [PyPDFLoader(x) for x in list_file_path]
    pages = []
    for loader in loaders:
        pages.extend(loader.load())
    # text_splitter = RecursiveCharacterTextSplitter(chunk_size = 600, chunk_overlap = 50)
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size = chunk_size, 
        chunk_overlap = chunk_overlap)
    doc_splits = text_splitter.split_documents(pages)
    return doc_splits


# Create vector database
def create_db(splits, collection_name):
    embedding = HuggingFaceEmbeddings()
    new_client = chromadb.EphemeralClient()
    vectordb = Chroma.from_documents(
        documents=splits,
        embedding=embedding,
        client=new_client,
        collection_name=collection_name,
        # persist_directory=default_persist_directory
    )
    return vectordb


# Load vector database
def load_db():
    embedding = HuggingFaceEmbeddings()
    vectordb = Chroma(
        # persist_directory=default_persist_directory, 
        embedding_function=embedding)
    return vectordb


# Initialize langchain LLM chain
def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
    progress(0.1, desc="Initializing HF tokenizer...")
    # HuggingFacePipeline uses local model
    # Note: it will download model locally...
    # tokenizer=AutoTokenizer.from_pretrained(llm_model)
    # progress(0.5, desc="Initializing HF pipeline...")
    # pipeline=transformers.pipeline(
    #     "text-generation",
    #     model=llm_model,
    #     tokenizer=tokenizer,
    #     torch_dtype=torch.bfloat16,
    #     trust_remote_code=True,
    #     device_map="auto",
    #     # max_length=1024,
    #     max_new_tokens=max_tokens,
    #     do_sample=True,
    #     top_k=top_k,
    #     num_return_sequences=1,
    #     eos_token_id=tokenizer.eos_token_id
    #     )
    # llm = HuggingFacePipeline(pipeline=pipeline, model_kwargs={'temperature': temperature})
    
    # HuggingFaceHub uses HF inference endpoints
    progress(0.5, desc="Initializing HF Hub...")
    # Use of trust_remote_code as model_kwargs
    # Warning: langchain issue
    # URL: https://github.com/langchain-ai/langchain/issues/6080
    if llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1":
        llm = HuggingFaceEndpoint(
            repo_id=llm_model, 
            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "load_in_8bit": True}
            temperature = temperature,
            max_new_tokens = max_tokens,
            top_k = top_k,
            load_in_8bit = True,
        )
    elif llm_model in ["HuggingFaceH4/zephyr-7b-gemma-v0.1","mosaicml/mpt-7b-instruct"]:
        raise gr.Error("LLM model is too large to be loaded automatically on free inference endpoint")
        llm = HuggingFaceEndpoint(
            repo_id=llm_model, 
            temperature = temperature,
            max_new_tokens = max_tokens,
            top_k = top_k,
        )
    elif llm_model == "microsoft/phi-2":
        # raise gr.Error("phi-2 model requires 'trust_remote_code=True', currently not supported by langchain HuggingFaceHub...")
        llm = HuggingFaceEndpoint(
            repo_id=llm_model, 
            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "trust_remote_code": True, "torch_dtype": "auto"}
            temperature = temperature,
            max_new_tokens = max_tokens,
            top_k = top_k,
            trust_remote_code = True,
            torch_dtype = "auto",
        )
    elif llm_model == "TinyLlama/TinyLlama-1.1B-Chat-v1.0":
        llm = HuggingFaceEndpoint(
            repo_id=llm_model, 
            # model_kwargs={"temperature": temperature, "max_new_tokens": 250, "top_k": top_k}
            temperature = temperature,
            max_new_tokens = 250,
            top_k = top_k,
        )
    elif llm_model == "meta-llama/Llama-2-7b-chat-hf":
        raise gr.Error("Llama-2-7b-chat-hf model requires a Pro subscription...")
        llm = HuggingFaceEndpoint(
            repo_id=llm_model, 
            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
            temperature = temperature,
            max_new_tokens = max_tokens,
            top_k = top_k,
        )
    else:
        llm = HuggingFaceEndpoint(
            repo_id=llm_model, 
            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "trust_remote_code": True, "torch_dtype": "auto"}
            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
            temperature = temperature,
            max_new_tokens = max_tokens,
            top_k = top_k,
        )
    
    progress(0.75, desc="Defining buffer memory...")
    memory = ConversationBufferMemory(
        memory_key="chat_history",
        output_key='answer',
        return_messages=True
    )
    # retriever=vector_db.as_retriever(search_type="similarity", search_kwargs={'k': 3})
    retriever=vector_db.as_retriever()
    progress(0.8, desc="Defining retrieval chain...")
    qa_chain = ConversationalRetrievalChain.from_llm(
        llm,
        retriever=retriever,
        chain_type="stuff", 
        memory=memory,
        # combine_docs_chain_kwargs={"prompt": your_prompt})
        return_source_documents=True,
        #return_generated_question=False,
        verbose=False,
    )
    progress(0.9, desc="Done!")
    return qa_chain


# Generate collection name for vector database
#  - Use filepath as input, ensuring unicode text
def create_collection_name(filepath):
    # Extract filename without extension
    collection_name = Path(filepath).stem
    # Fix potential issues from naming convention
    ## Remove space
    collection_name = collection_name.replace(" ","-") 
    ## ASCII transliterations of Unicode text
    collection_name = unidecode(collection_name)
    ## Remove special characters
    #collection_name = re.findall("[\dA-Za-z]*", collection_name)[0]
    collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name)
    ## Limit length to 50 characters
    collection_name = collection_name[:50]
    ## Minimum length of 3 characters
    if len(collection_name) < 3:
        collection_name = collection_name + 'xyz'
    ## Enforce start and end as alphanumeric character
    if not collection_name[0].isalnum():
        collection_name = 'A' + collection_name[1:]
    if not collection_name[-1].isalnum():
        collection_name = collection_name[:-1] + 'Z'
    print('Filepath: ', filepath)
    print('Collection name: ', collection_name)
    return collection_name


# Initialize database
def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()):
    # Create list of documents (when valid)
    list_file_path = [x.name for x in list_file_obj if x is not None]
    # Create collection_name for vector database
    progress(0.1, desc="Creating collection name...")
    collection_name = create_collection_name(list_file_path[0])
    progress(0.25, desc="Loading document...")
    # Load document and create splits
    doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
    # Create or load vector database
    progress(0.5, desc="Generating vector database...")
    # global vector_db
    vector_db = create_db(doc_splits, collection_name)
    progress(0.9, desc="Done!")
    return vector_db, collection_name, "Complete!"


def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
    # print("llm_option",llm_option)
    llm_name = list_llm[llm_option]
    print("llm_name: ",llm_name)
    qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress)
    return qa_chain, "Complete!"


def format_chat_history(message, chat_history):
    formatted_chat_history = []
    for user_message, bot_message in chat_history:
        formatted_chat_history.append(f"User: {user_message}")
        formatted_chat_history.append(f"Assistant: {bot_message}")
    return formatted_chat_history
    

def conversation(qa_chain, message, history):
    formatted_chat_history = format_chat_history(message, history)
    #print("formatted_chat_history",formatted_chat_history)
   
    # Generate response using QA chain
    response = qa_chain({"question": message, "chat_history": formatted_chat_history})
    response_answer = response["answer"]
    if response_answer.find("Helpful Answer:") != -1:
        response_answer = response_answer.split("Helpful Answer:")[-1]
    response_sources = response["source_documents"]
    response_source1 = response_sources[0].page_content.strip()
    response_source2 = response_sources[1].page_content.strip()
    response_source3 = response_sources[2].page_content.strip()
    # Langchain sources are zero-based
    response_source1_page = response_sources[0].metadata["page"] + 1
    response_source2_page = response_sources[1].metadata["page"] + 1
    response_source3_page = response_sources[2].metadata["page"] + 1
    # print ('chat response: ', response_answer)
    # print('DB source', response_sources)
    
    # Append user message and response to chat history
    new_history = history + [(message, response_answer)]
    # return gr.update(value=""), new_history, response_sources[0], response_sources[1] 
    return qa_chain, gr.update(value=""), new_history, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page
    

def upload_file(file_obj):
    list_file_path = []
    for idx, file in enumerate(file_obj):
        file_path = file_obj.name
        list_file_path.append(file_path)
    # print(file_path)
    # initialize_database(file_path, progress)
    return list_file_path


def demo():
    with gr.Blocks(theme="base") as demo:
        vector_db = gr.State()
        qa_chain = gr.State()
        collection_name = gr.State()
        
        gr.Markdown(
        """<center><h2>PDF-based chatbot</center></h2>
        <h3>Ask any questions about your PDF documents</h3>""")
        gr.Markdown(
        """<b>Note:</b> This AI assistant, using Langchain and open-source LLMs, performs retrieval-augmented generation (RAG) from your PDF documents. \
        The user interface explicitely shows multiple steps to help understand the RAG workflow. 
        This chatbot takes past questions into account when generating answers (via conversational memory), and includes document references for clarity purposes.<br>
        <br><b>Warning:</b> This space uses the free CPU Basic hardware from Hugging Face. Some steps and LLM models used below (free inference endpoints) can take some time to generate a reply.
        """)
        
        with gr.Tab("Step 1 - Upload PDF"):
            with gr.Row():
                document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload your PDF documents (single or multiple)")
                # upload_btn = gr.UploadButton("Loading document...", height=100, file_count="multiple", file_types=["pdf"], scale=1)
        
        with gr.Tab("Step 2 - Process document"):
            with gr.Row():
                db_btn = gr.Radio(["ChromaDB"], label="Vector database type", value = "ChromaDB", type="index", info="Choose your vector database")
            with gr.Accordion("Advanced options - Document text splitter", open=False):
                with gr.Row():
                    slider_chunk_size = gr.Slider(minimum = 100, maximum = 1000, value=600, step=20, label="Chunk size", info="Chunk size", interactive=True)
                with gr.Row():
                    slider_chunk_overlap = gr.Slider(minimum = 10, maximum = 200, value=40, step=10, label="Chunk overlap", info="Chunk overlap", interactive=True)
            with gr.Row():
                db_progress = gr.Textbox(label="Vector database initialization", value="None")
            with gr.Row():
                db_btn = gr.Button("Generate vector database")
            
        with gr.Tab("Step 3 - Initialize QA chain"):
            with gr.Row():
                llm_btn = gr.Radio(list_llm_simple, \
                    label="LLM models", value = list_llm_simple[0], type="index", info="Choose your LLM model")
            with gr.Accordion("Advanced options - LLM model", open=False):
                with gr.Row():
                    slider_temperature = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.7, step=0.1, label="Temperature", info="Model temperature", interactive=True)
                with gr.Row():
                    slider_maxtokens = gr.Slider(minimum = 224, maximum = 4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", interactive=True)
                with gr.Row():
                    slider_topk = gr.Slider(minimum = 1, maximum = 10, value=3, step=1, label="top-k samples", info="Model top-k samples", interactive=True)
            with gr.Row():
                llm_progress = gr.Textbox(value="None",label="QA chain initialization")
            with gr.Row():
                qachain_btn = gr.Button("Initialize Question Answering chain")

        with gr.Tab("Step 4 - Chatbot"):
            chatbot = gr.Chatbot(height=300)
            with gr.Accordion("Advanced - Document references", open=False):
                with gr.Row():
                    doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20)
                    source1_page = gr.Number(label="Page", scale=1)
                with gr.Row():
                    doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20)
                    source2_page = gr.Number(label="Page", scale=1)
                with gr.Row():
                    doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20)
                    source3_page = gr.Number(label="Page", scale=1)
            with gr.Row():
                msg = gr.Textbox(placeholder="Type message (e.g. 'What is this document about?')", container=True)
            with gr.Row():
                submit_btn = gr.Button("Submit message")
                clear_btn = gr.ClearButton([msg, chatbot], value="Clear conversation")
            
        # Preprocessing events
        #upload_btn.upload(upload_file, inputs=[upload_btn], outputs=[document])
        db_btn.click(initialize_database, \
            inputs=[document, slider_chunk_size, slider_chunk_overlap], \
            outputs=[vector_db, collection_name, db_progress])
        qachain_btn.click(initialize_LLM, \
            inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], \
            outputs=[qa_chain, llm_progress]).then(lambda:[None,"",0,"",0,"",0], \
            inputs=None, \
            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
            queue=False)

        # Chatbot events
        msg.submit(conversation, \
            inputs=[qa_chain, msg, chatbot], \
            outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
            queue=False)
        submit_btn.click(conversation, \
            inputs=[qa_chain, msg, chatbot], \
            outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
            queue=False)
        clear_btn.click(lambda:[None,"",0,"",0,"",0], \
            inputs=None, \
            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
            queue=False)
    demo.queue().launch(debug=True)


if __name__ == "__main__":
    demo()


in the above 2 code, code 1 is my app code & code 2 is unknow app code. when in my code i run any llm model (except gemini) which should use huggingface token that was not working showing this error "LLM initialization failed: LLM initialization failed: 1 validation error for HuggingFaceEndpoint
__root__
  Could not authenticate with huggingface_hub. Please check your API token. (type=value_error)"

but in code 2 they also use huggingface token & its llm, which work perfectly.

so, fix the issue which is present in code 1 with the help of code 2. keep in mind code 1 others code (except which i mention above)  work fine.
Note: give me complete correct code without skip any code.