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| import gradio as gr | |
| from gradio_client import Client | |
| from huggingface_hub import InferenceClient | |
| import random | |
| ss_client = Client("https://xilixmeaty40-testing.hf.space/") | |
| with open("models.txt", "r") as file: | |
| models = file.read().splitlines() | |
| clients = [InferenceClient(model) for model in models] | |
| VERBOSE = False | |
| def load_models(inp): | |
| if VERBOSE: | |
| print(type(inp)) | |
| print(inp) | |
| print(models[inp]) | |
| return gr.update(label=models[inp]) | |
| def format_prompt(message, history, cust_p): | |
| prompt = "" | |
| if history: | |
| for user_prompt, bot_response in history: | |
| prompt += f"<start_of_turn>user{user_prompt}<end_of_turn>" | |
| prompt += f"<start_of_turn>model{bot_response}<end_of_turn>" | |
| if VERBOSE: | |
| print(prompt) | |
| prompt += cust_p.replace("USER_INPUT", message) | |
| return prompt | |
| def chat_inf(system_prompt, prompt, history, memory, client_choice, seed, temp, tokens, top_p, rep_p, chat_mem, cust_p): | |
| hist_len = 0 | |
| client = clients[int(client_choice) - 1] | |
| if not history: | |
| history = [] | |
| if not memory: | |
| memory = [] | |
| if memory: | |
| for ea in memory[0 - chat_mem:]: | |
| hist_len += len(str(ea)) | |
| in_len = len(system_prompt + prompt) + hist_len | |
| if (in_len + tokens) > 8000: | |
| history.append((prompt, "Wait, that's too many tokens, please reduce the 'Chat Memory' value, or reduce the 'Max new tokens' value")) | |
| yield history, memory | |
| else: | |
| generate_kwargs = dict( | |
| temperature=temp, | |
| max_new_tokens=tokens, | |
| top_p=top_p, | |
| repetition_penalty=rep_p, | |
| do_sample=True, | |
| seed=seed, | |
| ) | |
| if system_prompt: | |
| formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", memory[0 - chat_mem:], cust_p) | |
| else: | |
| formatted_prompt = format_prompt(prompt, memory[0 - chat_mem:], cust_p) | |
| stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True) | |
| output = "" | |
| for response in stream: | |
| output += response.token.text | |
| yield [(prompt, output)], memory | |
| history.append((prompt, output)) | |
| memory.append((prompt, output)) | |
| yield history, memory | |
| def get_screenshot(chat: list, height=5000, width=600, chatblock=[], theme="light", wait=3000, header=True): | |
| tog = 0 | |
| if chatblock: | |
| tog = 3 | |
| result = ss_client.predict(str(chat), height, width, chatblock, header, theme, wait, api_name="/run_script") | |
| out = f'https://xilixmeaty40-testing.hf.space/file={result[tog]}' | |
| return out | |
| def clear_fn(): | |
| return None, None, None, None | |
| rand_val = random.randint(1, 1111111111111111) | |
| def check_rand(inp, val): | |
| if inp: | |
| return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=random.randint(1, 1111111111111111)) | |
| else: | |
| return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=int(val)) | |
| with gr.Blocks() as app: | |
| memory = gr.State() | |
| gr.HTML("""<center><h1 style='font-size:xx-large;'>Google Gemma Models</h1><br><h3>running on Huggingface Inference Client</h3><br><h7>EXPERIMENTAL""") | |
| chat_b = gr.Chatbot(height=500) | |
| with gr.Group(): | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| inp = gr.Textbox(label="Prompt") | |
| sys_inp = gr.Textbox(label="System Prompt (optional)") | |
| with gr.Accordion("Prompt Format", open=False): | |
| custom_prompt = gr.Textbox(label="Modify Prompt Format", info="For testing purposes. 'USER_INPUT' is where 'SYSTEM_PROMPT, PROMPT' will be placed", lines=3, value="<start_of_turn>userUSER_INPUT<end_of_turn><start_of_turn>model") | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| btn = gr.Button("Chat") | |
| with gr.Column(scale=1): | |
| with gr.Group(): | |
| stop_btn = gr.Button("Stop") | |
| clear_btn = gr.Button("Clear") | |
| client_choice = gr.Dropdown(label="Models", type='index', choices=[c for c in models], value=models[0], interactive=True) | |
| with gr.Column(scale=1): | |
| with gr.Group(): | |
| rand = gr.Checkbox(label="Random Seed", value=True) | |
| seed = gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, step=1, value=rand_val) | |
| tokens = gr.Slider(label="Max new tokens", value=300000, minimum=0, maximum=800000, step=64, interactive=True, visible=True, info="The maximum number of tokens") | |
| temp = gr.Slider(label="Temperature", step=0.01, minimum=0.01, maximum=1.0, value=0.49) | |
| top_p = gr.Slider(label="Top-P", step=0.01, minimum=0.01, maximum=1.0, value=0.49) | |
| rep_p = gr.Slider(label="Repetition Penalty", step=0.01, minimum=0.1, maximum=2.0, value=0.99) | |
| chat_mem = gr.Number(label="Chat Memory", info="Number of previous chats to retain", value=4) | |
| with gr.Accordion(label="Screenshot", open=False): | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| im_btn = gr.Button("Screenshot") | |
| img = gr.Image(type='filepath') | |
| with gr.Column(scale=1): | |
| with gr.Row(): | |
| im_height = gr.Number(label="Height", value=5000) | |
| im_width = gr.Number(label="Width", value=500) | |
| wait_time = gr.Number(label="Wait Time", value=3000) | |
| theme = gr.Radio(label="Theme", choices=["light", "dark"], value="light") | |
| chatblock = gr.Dropdown(label="Chatblocks", info="Choose specific blocks of chat", choices=[c for c in range(1, 40)], multiselect=True) | |
| client_choice.change(load_models, client_choice, [chat_b]) | |
| app.load(load_models, client_choice, [chat_b]) | |
| im_go = im_btn.click(get_screenshot, [chat_b, im_height, im_width, chatblock, theme, wait_time], img) | |
| chat_sub = inp.submit(check_rand, [rand, seed], seed).then(chat_inf, [sys_inp, inp, chat_b, memory, client_choice, seed, temp, tokens, top_p, rep_p, chat_mem, custom_prompt], [chat_b, memory]) | |
| go = btn.click(check_rand, [rand, seed], seed).then(chat_inf, [sys_inp, inp, chat_b, memory, client_choice, seed, temp, tokens, top_p, rep_p, chat_mem, custom_prompt], [chat_b, memory]) | |
| stop_btn.click(None, None, None, cancels=[go, im_go, chat_sub]) | |
| clear_btn.click(clear_fn, None, [inp, sys_inp, chat_b, memory]) | |
| app.queue(default_concurrency_limit=10).launch() | |