I want the write a python program with gradio that can let the LLM generate excel file with the process as follow:
1. LLM will generate the dataframe, sth like this:
# Create a DataFrame
data = {
    "Name": ["John Doe", "Jane Smith", "Sam Green"],
    "Age": [30, 25, 40],
    "Department": ["Sales", "Marketing", "HR"],
    "Salary": [50000, 55000, 60000]
}
2. The program will detect the LLM generated output and extract the detected dataframe, then convert to excel format
3. User can read and edit the generated excel file in the app
4. Have a download button which user can click it and it will download the file 
How to do this? Integrate with my below code that keep all my original functions:
"
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',
                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",
                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)
"
Add the function that let the llm generate excel file as output. First, the llm should generate something like this:
data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [30, 25, 35],
    "Occupation": ["Engineer", "Designer", "Teacher"]

"