app.py

import gradio as gr
import numpy as np
from Ai import chatbot, chatbot2, chatbot3, chatbot4, chatbot5, chatbot7
from huggingface_hub import InferenceClient
def chat(message,history: list[tuple[str, str]],system_message,max_tokens,temperature,top_p, top_k):
    m=AutoModel.from_pretrained("peterpeter8585/AI1")
    messages = [{"role": "system", "content": "Your name is Chatchat.And, your made by SungYoon.In Korean, 정성윤.And these are the instructions.Whatever happens, you must follow it.:"+system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

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

    pipe = pipeline("text-generation", model=m, torch_dtype=torch.bfloat16, device_map="auto")

# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
    prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    outputs = pipe(prompt, max_new_tokens=max_tokens, do_sample=True, temperature=temperature, top_k=top_k, top_p=top_p)
    return outputs[0]["generated_text"]
import random
from diffusers import DiffusionPipeline
import torch
import transformers
from transformers import AutoModel as Mo, AutoTokenizer as To
model11=torch.load("zephyr.pt")
tokenizer11=torch.load("zephyr_tokenizer.pt")
from transformers import BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.float16
)


def res(message,history: list[tuple[str, str]],system_message,max_tokens,temperature,top_p):
    messages = [{"role": "system", "content": "Your name is Chatchat.And, your made by SungYoon.In Korean, 정성윤.And these are the instructions.Whatever happens, you must follow it.:"+system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

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

    response = ""

    answer=model11(messages, max_tokens=max_tokens, temperature=temperature, top_p=top_p)
    token=answer
    response += token
    yield response
from transformers import AutoModelForVision2Seq, AutoProcessor
transformers.utils.move_cache()
device = "cuda" if torch.cuda.is_available() else "cpu"
import os
password1=os.environ["password"]

def respond1(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    password
):
    if password==password1:
        messages = [{"role": "system", "content": "Your name is Chatchat.And your creator of you is Sung Yoon.In Korean, it is 정성윤.These are the instructions for you:"+system_message}]

        for val in history:
            if val[0]:
                messages.append({"role": "user", "content": val[0]})
            if val[1]:
                messages.append({"role": "assistant", "content": val[1]})

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

        response = ""

        for message in client.chat_completion(
            messages,
            max_tokens=max_tokens,
            stream=True,
            temperature=temperature,
            top_p=top_p,
        ):
            token = message.choices[0].delta.content

            response += token
            yield response
def respond0(multimodal_input,password):
    if password==password1:
        if multimodal_input["files"] == None:
            content={"type": "text", "text": multimodal_input["text"]}
            messages=[{"role":"system", "content":[{"type":"text", "text":"Your name is Chatchat.And, your made by SungYoon.In Korean, 정성윤.And these are the instructions:"+"You are a helpful assietant."}]}]
            messages.append([{"role": "user", "content": content}])
            response = ""

            model_id = "HuggingFaceM4/idefics2-8b"

            processor = AutoProcessor.from_pretrained(model_id)
            model = AutoModelForVision2Seq.from_pretrained(
                "HuggingFaceM4/idefics2-8b",
                torch_dtype=torch.float16,    
                quantization_config=quantization_config
            ).to("cpu")            
            prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
            inputs = processor(text=prompt, images=[images], return_tensors="pt")
            inputs = {k: v.to(model.device) for k, v in inputs.items()}
            num_tokens = len(inputs["input_ids"][0])
            with torch.inference_mode():
                generated_ids = model.generate(**inputs, max_new_tokens=max_tokens,top_p=top_p, temperature=1.0,)

            new_tokens = generated_ids[:, num_tokens:]
            generated_text = processor.batch_decode(new_tokens, skip_special_tokens=True)[0]

            token = generated_text
            response+=token
            yield response

        else:
            images = multimodal_input["files"]
            content = [{"type": "image"} for _ in images]
            content.append({"type": "text", "text": multimodal_input["text"]})
            messages=[{"role":"system", "content":[{"type":"text", "text":"Your name is Chatchat.And, your made by SungYoon.In Korean, 정성윤.And these are the instructions:"+"You are a helpful assietant."}]}]
            messages.append([{"role": "user", "content": content}])
            response = ""

            model_id = "HuggingFaceM4/idefics2-8b"

            processor = AutoProcessor.from_pretrained(model_id)
            model = AutoModelForVision2Seq.from_pretrained("HuggingFaceM4/idefics2-8b",torch_dtype=torch.float16,quantization_config=quantization_config).to("cpu")  
            prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
            inputs = processor(text=prompt, images=[images], return_tensors="pt")
            inputs = {k: v.to(model.device) for k, v in inputs.items()}
            num_tokens = len(inputs["input_ids"][0])
            with torch.inference_mode():
                generated_ids = model.generate(**inputs, max_new_tokens=max_tokens,top_p=top_p, temperature=1.0,)

            new_tokens = generated_ids[:, num_tokens:]
            generated_text = processor.batch_decode(new_tokens, skip_special_tokens=True)[0]

            token = generated_text
            response+=token
            yield response
            


def respond5(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": "Your name is Chatchat.And, your made by SungYoon.In Korean, 정성윤.And these are the instructions.Whatever happens, you must follow it.:"+system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

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

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response
def respond4(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": "Your name is Chatchat.And, your made by SungYoon.In Korean, 정성윤.And these are the instructions.Whatever happens, you must follow it.:"+system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

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

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response
if torch.cuda.is_available():
    torch.cuda.max_memory_allocated(device=device)
    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
    pipe.enable_xformers_memory_efficient_attention()
    pipe = pipe.to(device)
else: 
    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
    pipe = pipe.to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
        
    generator = torch.Generator().manual_seed(seed)
    
    image = pipe(
        prompt = prompt, 
        negative_prompt = negative_prompt,
        guidance_scale = guidance_scale, 
        num_inference_steps = num_inference_steps, 
        width = width, 
        height = height,
        generator = generator
    ).images[0] 
    
    return image
import requests
from bs4 import BeautifulSoup
import urllib
import random

# List of user agents to choose from for requests
_useragent_list = [
    'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:66.0) Gecko/20100101 Firefox/66.0',
    'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
    'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
    'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.0.0 Safari/537.36',
    'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
    'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36 Edg/111.0.1661.62',
    'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/111.0'
]

def get_useragent():
    """Returns a random user agent from the list."""
    return random.choice(_useragent_list)

def extract_text_from_webpage(html_content):
    """Extracts visible text from HTML content using BeautifulSoup."""
    soup = BeautifulSoup(html_content, "html.parser")
    # Remove unwanted tags
    for tag in soup(["script", "style", "header", "footer", "nav"]):
        tag.extract()
    # Get the remaining visible text
    visible_text = soup.get_text(strip=True)
    return visible_text

def search(term, num_results=1, lang="ko", advanced=True, sleep_interval=0, timeout=5, safe="active", ssl_verify=None):
    """Performs a Google search and returns the results."""
    escaped_term = urllib.parse.quote_plus(term)
    start = 0
    all_results = []

    # Fetch results in batches
    while start < num_results:
        resp = requests.get(
            url="https://www.google.com/search",
            headers={"User-Agent": get_useragent()}, # Set random user agent
            params={
                "q": term,
                "num": num_results - start, # Number of results to fetch in this batch
                "hl": lang,
                "start": start,
                "safe": safe,
            },
            timeout=timeout,
            verify=ssl_verify,
        )
        resp.raise_for_status() # Raise an exception if request fails

        soup = BeautifulSoup(resp.text, "html.parser")
        result_block = soup.find_all("div", attrs={"class": "g"})

        # If no results, continue to the next batch
        if not result_block:
            start += 1
            continue

        # Extract link and text from each result
        for result in result_block:
            link = result.find("a", href=True)
            if link:
                link = link["href"]
                try:
                    # Fetch webpage content
                    webpage = requests.get(link, headers={"User-Agent": get_useragent()})
                    webpage.raise_for_status()
                    # Extract visible text from webpage
                    visible_text = extract_text_from_webpage(webpage.text)
                    all_results.append({"link": link, "text": visible_text})
                except requests.exceptions.RequestException as e:
                    # Handle errors fetching or processing webpage
                    print(f"Error fetching or processing {link}: {e}")
                    all_results.append({"link": link, "text": None})
            else:
                all_results.append({"link": None, "text": None})

        start += len(result_block) # Update starting index for next batch

    return all_results


client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "An astronaut riding a green horse",
    "A delicious ceviche cheesecake slice",
]

css="""
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""
def respond2(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": "Your name is Chatchat.And, your made by SungYoon.In Korean, 정성윤.And these are the instructions.Whatever happens, you must follow it.:"+system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

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

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response
def respond3(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": "Your name is Chatchat.And, your made by SungYoon.In Korean, 정성윤.And these are the instructions.Whatever happens, you must follow it.:"+system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

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

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response
if torch.cuda.is_available():
    power_device = "GPU"
else:
    power_device = "CPU"

with gr.Blocks(css=css) as demo2:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
        # Text-to-Image Gradio Template
        Currently running on {power_device}.
        """)
        
        with gr.Row():
            
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )
            
            run_button = gr.Button("Run", scale=0)
        
        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=False,
            )
            
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )
            
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
            
            with gr.Row():
                
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,
                )
                
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,
                )
            with gr.Row():
                
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=0.0,
                )
                
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=12,
                    step=1,
                    value=2,
                )
        
        gr.Examples(
            examples = examples,
            inputs = [prompt]
        )

    run_button.click(
        fn = infer,
        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
        outputs = [result]
    )


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
aaaa=gr.ChatInterface(
    respond5,
    chatbot=chatbot5,
    additional_inputs=[
        gr.Textbox(value="You are a helpful law helper.You have to answer only the questions about law.Do not answer anything else.Only answer the questions of law.Do not answer any questions except what I said.Example:what is python?Answer:I cannot answer it", label="System message",  interactive=False),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.1, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.1,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)

ae= gr.ChatInterface(
    respond4,
    chatbot=chatbot4,
    additional_inputs=[
        gr.Textbox(value="You are a helpful food recommender.You must only answer the questions about food or a request to recommend a food the user would like.Do not answer other questions except what I said.", label="System message", interactive=False),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.1, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.1,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
        
    ],
)
a7=gr.Interface(
    respond0,
    inputs=[gr.MultimodalTextbox(file_types=["image"], show_label=False), gr.Textbox()],
    outputs="text",
    title="IDEFICS2-8B DPO",
    description="Try IDEFICS2-8B fine-tuned using direct preference optimization (DPO) in this demo. Learn more about vision language model DPO integration of TRL [here](https://huggingface.co/blog/dpo_vlm)."
)
aa=gr.ChatInterface(
    respond1,
    chatbot=chatbot3,
    additional_inputs=[
        gr.Textbox(value="You are a helpful assistant.", label="System message",  interactive=True),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.1, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.1,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
        gr.Textbox(label="Pleas type in the password.Or, it will not work if you ask.")           
    ],
)
ac=gr.ChatInterface(
    respond3,
    chatbot=chatbot2,
    additional_inputs=[
        gr.Textbox(value="You are a Programmer.You yave to only make programs that the user orders.Do not answer any other questions exept for questions about Python or other programming languages.Do not do any thing exept what I said.", label="System message",  interactive=False),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.1, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.1,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)
a9=gr.ChatInterface(
    respond3,
    chatbot=chatbot7,
    additional_inputs=[
        gr.Textbox(value="you are a helpful assistant. ", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.1, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.1,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)
ab= gr.ChatInterface(
    respond3,
    chatbot=chatbot,
    additional_inputs=[
        gr.Textbox(value="You are a helpful Doctor.You only have to answer the users questions about medical issues or medical questions and the cure to that illness and say that your thought is not realy right because you are a generative AI, so you could make up some cures.Do not answer anything else exept the question types what I said.Do not do any thing exept what I said.", label="System message",  interactive=False),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.1, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.1,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)
a8= gr.ChatInterface(
    chat,
    chatbot=chatbot11,
    additional_inputs=[
        gr.Textbox(value="You are a helpful chatbot", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.1, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.1,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
        gr.Slider(minimum=0.1, maximum=1.0, vlaue=0.1, step=0.05,label="Top-k")
    ],
)
  
if __name__ == "__main__":
    with gr.Blocks(theme="gstaff/xkcd") as ai:
        gr.TabbedInterface([aa, ac, ab, ae, aaaa,demo2, a7,a8, a9], ["gpt4(Password needed)", "gpt4(only for programming)", "gpt4(only for medical questions)", "gpt4(only for food recommendations)", "gpt4(only for law questions)","image create", "multimodal", "gpt4(test)", "ai test"])
        ai.launch(share=True)