Read my python code and fix the error:
"
# Pdf, work

import os
import time
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, BitsAndBytesConfig
import gradio as gr
from threading import Thread
from PyPDF2 import PdfReader  # Import PyPDF2 for PDF text extraction

MODEL_PATH = r"C:\Users\shermanhonsf\Desktop\Python\llama_develop\model\Llama3.2-1B-Instruct-HF-Model"  # Your model path

TITLE = """
Chat with Llama 3.2 1B
"""

PLACEHOLDER = """
Hi, I'm Llama. Ask me anything or upload a PDF to chat about its content.
"""

CSS = """
.duplicate-button {
    margin: auto !important;
    color: white !important;
    background: black !important;
    border-radius: 100vh !important;
}
h3 {
    text-align: center;
}
"""

# Set the device to "cuda" if you have a compatible GPU, otherwise "cpu"
device = "cuda" if torch.cuda.is_available() else "cpu"

# Configure quantization if needed (optional)
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4"
)

# Load the tokenizer and model from the local directory
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_PATH,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    # quantization_config=quantization_config
)

# Move the model to the appropriate device
model.to(device)


def extract_pdf_text(pdf_path):
    """Extract all text from a PDF file."""
    try:
        reader = PdfReader(pdf_path)
        text = ""
        for page in reader.pages:
            text += page.extract_text()
        return text
    except Exception as e:
        return f"Error reading PDF: {e}"


def stream_chat(message: str, history: list, system_prompt: str ="You are a helpful assistant. When asked to generate tabular data, please follow these rules:\n"
                    "1. Produce the specified data as a CSV table with a header row.\n"
                    "2. Ensure that fields in the CSV are separated by commas (',').\n"
                    "3. Enclose the CSV output with markers: \n"
                    "   - Begin with `#START_CSV`.\n"
                    "   - End with `#END_CSV`.\n",
                temperature: float = 0.8, max_new_tokens: int = 1024, top_p: float = 1.0, top_k: int = 20, penalty: float = 1.2, pdf_text: str = ""):
    print(f'message: {message}')
    print(f'history: {history}')
    print(f'system_prompt: {system_prompt}')
    print(f'temperature: {temperature}')
    print(f'max_new_tokens: {max_new_tokens}')
    print(f'top_p: {top_p}')
    print(f'top_k: {top_k}')
    print(f'penalty: {penalty}')
    print(f'Message: {message}')
    print(f'History: {history}')
    print(f'PDF Text: {pdf_text}')

    conversation = [
        {"role": "system", "content": system_prompt}
    ]

    # Add PDF content to the system prompt if provided
    if pdf_text.strip():
        pdf_summary = f"The user uploaded a PDF with the following content:\n{pdf_text[:2000]}...\n(This is a truncated summary if the text is too long.)"
        conversation.append({"role": "system", "content": pdf_summary})

    for prompt, answer in history:
        conversation.extend([
            {"role": "user", "content": prompt},
            {"role": "assistant", "content": answer},
        ])

    conversation.append({"role": "user", "content": message})

    # Prepare input IDs
    input_ids = tokenizer.apply_chat_template(
        conversation,
        # tokenize=True,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to(device)

    streamer = TextIteratorStreamer(
        tokenizer,
        timeout=60.0,
        skip_prompt=True,
        skip_special_tokens=True
    )

    generate_kwargs = dict(
        input_ids=input_ids,
        max_new_tokens=max_new_tokens,
        do_sample=False if temperature == 0 else True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        eos_token_id=[128001, 128008, 128009],
        streamer=streamer,
    )

    # Generate the response in a background thread
    with torch.no_grad():
        thread = Thread(target=model.generate, kwargs=generate_kwargs)
        thread.start()

    # Stream the response back to the user
    buffer = ""
    for new_text in streamer:
        buffer += new_text
        yield buffer


# Create the Gradio interface
chatbot = gr.Chatbot(height=600, show_copy_button=True, layout="panel", placeholder=PLACEHOLDER)

with gr.Blocks(css=CSS) as demo:
    gr.HTML(TITLE)
    
    # Add file uploader for PDF input
    pdf_file = gr.File(label="Upload PDF", type="filepath")
    pdf_textbox = gr.Textbox(label="Extracted PDF Text", placeholder="PDF content will appear here after upload.", lines=10, interactive=False)
    
    def process_pdf(file):
        if file is not None:
            pdf_text = extract_pdf_text(file.name)
            return pdf_text
        return "No PDF uploaded."

    pdf_file.change(process_pdf, inputs=pdf_file, outputs=pdf_textbox)
    
    gr.ChatInterface(
        fn=stream_chat,
        chatbot=chatbot,
        fill_height=True,
        additional_inputs_accordion=gr.Accordion(
            label="⚙️ Parameters",
            open=False,
            render=False
        ),
        additional_inputs=[
            gr.Textbox(
                value="You are a helpful assistant. When asked to generate tabular data, please follow these rules:\n"
                    "1. Produce the specified data as a CSV table with a header row.\n"
                    "2. Ensure that fields in the CSV are separated by commas (',').\n"
                    "3. Enclose the CSV output with markers: \n"
                    "   - Begin with `#START_CSV`.\n"
                    "   - End with `#END_CSV`.\n",
                label="System Prompt",
                render=False,
            ),
            gr.Slider(
                minimum=0,
                maximum=1,
                step=0.1,
                value=0.8,
                label="Temperature",
                render=False,
            ),
            gr.Slider(
                minimum=128,
                maximum=8192,
                step=1,
                value=1024,
                label="Max new tokens",
                render=False,
            ),
            gr.Slider(
                minimum=0.0,
                maximum=1.0,
                step=0.1,
                value=1.0,
                label="Top_p",
                render=False,
            ),
            gr.Slider(
                minimum=1,
                maximum=20,
                step=1,
                value=20,
                label="Top_k",
                render=False,
            ),
            gr.Slider(
                minimum=0.0,
                maximum=2.0,
                step=0.1,
                value=1.2,
                label="Repetition penalty",
                render=False,
            ),
            pdf_textbox  # Pass the extracted PDF text as an additional input
        ],
        examples=[
            ["What are the main themes explored in the movie 'Dune', and how do they reflect contemporary issues? "],
            ["What are 3 practical techniques I can use to prioritize tasks more effectively in a busy work schedule? "],
            ["How does blockchain technology work, and what are its potential applications beyond cryptocurrencies? "],
            ["Show me a code snippet of a website's sticky header in CSS and JavaScript."],
        ],
        cache_examples=False,
    )

if __name__ == "__main__":
    demo.launch(debug=True)
"
C:\Users\shermanhonsf\Desktop\Python\.conda\Lib\site-packages\gradio\components\chatbot.py:223: UserWarning: You have not specified a value for the `type` parameter. Defaulting to the 'tuples' format for chatbot messages, but this is deprecated and will be removed in a future version of Gradio. Please set type='messages' instead, which uses openai-style 'role' and 'content' keys.
  warnings.warn(
Traceback (most recent call last):
  File "c:\Users\shermanhonsf\Desktop\Python\llama_develop\llama_with_excel_try.py", line 229, in <module>
    chat_interface = gr.ChatInterface(
                     ^^^^^^^^^^^^^^^^^
TypeError: ChatInterface.__init__() got an unexpected keyword argument 'history'