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| import gradio as gr | |
| import numpy as np | |
| from huggingface_hub import InferenceClient | |
| import random | |
| from diffusers import DiffusionPipeline | |
| import torch | |
| import transformers | |
| transformers.utils.move_cache() | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| import os | |
| os.environ["password"] | |
| 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") | |
| 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 | |
| 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 | |
| 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 | |
| """ | |
| aa = gr.ChatInterface( | |
| respond1, | |
| additional_inputs=[ | |
| gr.Textbox(value="You are a friendly 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=0.7, step=0.1, label="Temperature"), | |
| gr.Slider( | |
| minimum=0.1, | |
| maximum=1.0, | |
| value=0.95, | |
| step=0.05, | |
| label="Top-p (nucleus sampling)", | |
| ), | |
| gr.Textbox() | |
| ], | |
| ) | |
| ab= gr.ChatInterface( | |
| respond2, | |
| 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=0.7, step=0.1, label="Temperature"), | |
| gr.Slider( | |
| minimum=0.1, | |
| maximum=1.0, | |
| value=0.95, | |
| step=0.05, | |
| label="Top-p (nucleus sampling)", | |
| ), | |
| ], | |
| ) | |
| if __name__ == "__main__": | |
| with gr.Blocks() as ai: | |
| gr.TabbedInterface([aa, demo2], ["gpt4", "image create"]) | |
| ai.queue(max_size=300) | |
| ai.launch() |