import gradio as gr import numpy as np from huggingface_hub import InferenceClient import random from diffusers import DiffusionPipeline import torch 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("mistralai/Mixtral-8x7B-Instruct-v0.1") def respond( message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p, ): messages = "[SYSTEM] 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 8f the user asks with English, you have to answer with English.Do not transelate it.[USER]"+system_message web_results = search(message) web2 = ' '.join([f"Link: {res['link']}\nText: {res['text']}\n\n" for res in web_results]) formatted_prompt = messages + message + "[WEB]" + str(web2) + "[OpenGPT 4o]" response = "" stream = client.text_generation(formatted_prompt, max_new_tokens=512, stream=True, details=True, return_full_text=False) token="".join([response.token.text for response in stream if response.token.text != ""]) 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.launch()