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| import gradio as gr | |
| from gradio_client import Client | |
| from huggingface_hub import InferenceClient | |
| import random | |
| from deep_translator import GoogleTranslator | |
| 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 load_models(inp): | |
| if VERBOSE==True: | |
| print(type(inp)) | |
| print(inp) | |
| print(models[inp]) | |
| #client_z.clear() | |
| #client_z.append(InferenceClient(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 += f"<start_of_turn>user\n{message}<end_of_turn>\n<start_of_turn>model\n" | |
| 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,translate_fa): | |
| #token max=8192 | |
| if(translate_fa == True): | |
| if(len(prompt) > 2000): | |
| translatedtext1 = GoogleTranslator(source='auto', target='en').translate(prompt[0:2000]) | |
| translatedtext2 = GoogleTranslator(source='auto', target='en').translate(prompt[2000:(len(prompt))]) | |
| prompt = translatedtext1 + translatedtext2 | |
| else: | |
| prompt = GoogleTranslator(source='auto', target='en').translate(prompt) | |
| print(client_choice) | |
| hist_len=0 | |
| client=clients[int(client_choice)-1] | |
| if not history: | |
| history = [] | |
| hist_len=0 | |
| if not memory: | |
| memory = [] | |
| mem_len=0 | |
| 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) | |
| chat = [ | |
| { "role": "user", "content": f"{formatted_prompt}" }, | |
| ] | |
| 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 | |
| if(translate_fa == True): | |
| output = GoogleTranslator(source='auto', target='fa').translate(output) | |
| history.append((prompt,output)) | |
| memory.append((prompt,output)) | |
| yield history,memory | |
| if VERBOSE==True: | |
| print("\n######### HIST "+str(in_len)) | |
| print("\n######### TOKENS "+str(tokens)) | |
| 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)) | |
| 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_fa = 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_inf,[sys_inp,inp,chat_b,memory,client_choice,seed,temp,tokens,top_p,rep_p,chat_mem,custom_prompt,translate_fa],[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,translate_fa],[chat_b,memory]) | |
| clear_btn.click(clear_fn,None,[inp,sys_inp,chat_b,memory]) | |
| app.queue(default_concurrency_limit=10).launch() |