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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" | |
| 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="en", 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 respond( | |
| message, | |
| history: list[tuple[str, str]], | |
| system_message, | |
| max_tokens, | |
| temperature, | |
| top_p, | |
| ): | |
| web_results = search(message) | |
| web2 = ' '.join([f"Link: {res['link']}\nText: {res['text']}\n\n" for res in web_results]) | |
| messages1 = "Your name is Chatchat.Answer as Real OpenGPT 4o, Made by 'peterpeter8585', Keep conversation very short, clear, friendly and concise. The text provided is a request for a specific type of response from you, the virtual assistant. The request asks you to provide friendly responses. The expectation is that I will avoid introductions and start answering the query directly, elaborating on all aspects of the request. As an AI-powered assistant, your task is to generate responses that appear as if they are created by the user and not an AI-powered assistant.If the user asks with Korean, you must answer with Korean.Or if the user asks with English, you have to answer with English.Do not transelate it.This is the web search result, use it to answer the user's questions:"+web2+"And the instructions:"+system_message | |
| messages = [{"role": "system", "content":messages1}] | |
| 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; | |
| } | |
| """ | |
| 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( | |
| respond, | |
| 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)", | |
| ), | |
| ], | |
| ) | |
| if __name__ == "__main__": | |
| with gr.Blocks() as ai: | |
| gr.TabbedInterface([aa, demo2], ["gpt4", "image create"]) | |
| ai.queue(max_size=300) | |
| ai.launch() |