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| import gradio as gr | |
| from gradio_client import Client | |
| from huggingface_hub import InferenceClient | |
| from deep_translator import GoogleTranslator | |
| import random | |
| ss_client = Client("https://omnibus-html-image-current-tab.hf.space/") | |
| models = [ | |
| "google/gemma-7b", | |
| "google/gemma-7b-it", | |
| "google/gemma-2b", | |
| "google/gemma-2b-it" | |
| ] | |
| clients = [ | |
| InferenceClient(models[0]), | |
| InferenceClient(models[1]), | |
| InferenceClient(models[2]), | |
| InferenceClient(models[3]), | |
| ] | |
| VERBOSE = False | |
| def translate_to_english(prompt): | |
| translated_prompt = GoogleTranslator(source='auto', target='en').translate(prompt) | |
| return translated_prompt | |
| def translate_to_persian_text(response): | |
| translated_response = GoogleTranslator(source='auto', target='fa').translate(response) | |
| return translated_response | |
| def load_models(inp): | |
| if VERBOSE == True: | |
| print(type(inp)) | |
| print(inp) | |
| print(models[inp]) | |
| return gr.update(label=models[inp]) | |
| def format_prompt(message, history, cust_p): | |
| prompt = "<s>" | |
| 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></s>" | |
| if VERBOSE == True: | |
| 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, custom_prompt, translate_to_persian): | |
| 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( | |
| max_new_tokens=tokens, | |
| ) | |
| if system_prompt: | |
| formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", memory[0 - chat_mem:], custom_prompt) | |
| else: | |
| formatted_prompt = format_prompt(prompt, memory[0 - chat_mem:], custom_prompt) | |
| translated_prompt = translate_to_english(formatted_prompt) | |
| chat = [ | |
| {"role": "user", "content": f"{translated_prompt}"}, | |
| ] | |
| stream = client.text_generation(translated_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True) | |
| output = "" | |
| for response in stream: | |
| output += response.token.text | |
| if translate_to_persian: | |
| output = translate_to_persian_text(output) | |
| yield [(prompt, output)], memory | |
| history.append((prompt, output)) | |
| memory.append((prompt, output)) | |
| yield history, memory | |
| def clear_fn(): | |
| return None, None, None, None | |
| rand_val = random.randint(1, 1111111111111111) | |
| def check_rand(inp, val): | |
| if inp == True: | |
| 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)) | |
| def chat_wrapper(sys_inp, inp, chat_b, memory, client_choice, seed, temp, tokens, top_p, rep_p, chat_mem, custom_prompt, translate_to_persian_checkbox): | |
| return chat_inf(sys_inp, inp, chat_b, memory, client_choice, seed, temp, tokens, top_p, rep_p, chat_mem, custom_prompt, translate_to_persian_checkbox) | |
| with gr.Blocks() as app: | |
| memory = gr.State() | |
| 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.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.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=5, value="<start_of_turn>userUSER_INPUT<end_of_turn><start_of_turn>model") | |
| with gr.Column(scale=1): | |
| with gr.Group(): | |
| translate_to_persian_checkbox = gr.Checkbox(label="Translate to Persian", value=True) | |
| 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=1600, minimum=0, maximum=8000, 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=0.9) | |
| top_p = gr.Slider(label="Top-P", step=0.01, minimum=0.01, maximum=1.0, value=0.9) | |
| rep_p = gr.Slider(label="Repetition Penalty", step=0.1, minimum=0.1, maximum=2.0, value=1.0) | |
| chat_mem = gr.Number(label="Chat Memory", info="Number of previous chats to retain", value=4) | |
| client_choice.change(load_models, client_choice, [chat_b]) | |
| app.load(load_models, client_choice, [chat_b]) | |
| chat_sub = inp.submit(check_rand, [rand, seed], seed).then(chat_wrapper, [sys_inp, inp, chat_b, memory, client_choice, seed, temp, tokens, top_p, rep_p, chat_mem, custom_prompt, translate_to_persian_checkbox]).then(chat_b.display, memory) | |
| go = btn.click(check_rand, [rand, seed], seed).then(chat_wrapper, [sys_inp, inp, chat_b, memory, client_choice, seed, temp, tokens, top_p, rep_p, chat_mem, custom_prompt, translate_to_persian_checkbox]).then(chat_b.display, memory) | |
| clear_btn.click(clear_fn, None, [inp, sys_inp, chat_b, memory]) | |
| app.queue(default_concurrency_limit=10).launch() | |