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| import gradio as gr | |
| from gradio_client import Client | |
| from huggingface_hub import InferenceClient | |
| 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" | |
| "meta-llama/Llama-2-7b-chat-hf", | |
| "codellama/CodeLlama-70b-Instruct-hf", | |
| "openchat/openchat-3.5-0106", | |
| "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO", | |
| "mistralai/Mixtral-8x7B-Instruct-v0.1", | |
| "mistralai/Mixtral-8x7B-Instruct-v0.2" | |
| ] | |
| '''clients=[ | |
| InferenceClient(models[0]), | |
| InferenceClient(models[1]), | |
| InferenceClient(models[2]), | |
| InferenceClient(models[3]), | |
| ]''' | |
| client_z=[] | |
| def load_models(inp): | |
| print(type(inp)) | |
| client_z.clear() | |
| if inp[0]: | |
| client_z[0]=InferenceClient(models[inp[0]]) | |
| if inp[1]: | |
| client_z[1]=InferenceClient(models[inp[1]]) | |
| if inp[2]: | |
| client_z[2]=InferenceClient(models[inp[2]]) | |
| if inp[3]: | |
| client_z[3]=InferenceClient(models[inp[3]]) | |
| return gr.update(label=models[inp[0]]) | |
| def format_prompt(message, history): | |
| prompt = "" | |
| if history: | |
| #<start_of_turn>userHow does the brain work?<end_of_turn><start_of_turn>model | |
| for user_prompt, bot_response in history: | |
| prompt += f"{user_prompt}\n" | |
| print(prompt) | |
| prompt += f"{bot_response}\n" | |
| print(prompt) | |
| prompt += f"<start_of_turn>user{message}<end_of_turn><start_of_turn>model" | |
| print(prompt) | |
| return prompt | |
| def chat_inf(system_prompt,prompt,history,client_choice,seed,temp,tokens,top_p,rep_p): | |
| #token max=8192 | |
| client=clients[int(client_choice)-1] | |
| if not history: | |
| history = [] | |
| hist_len=0 | |
| if history: | |
| hist_len=len(history) | |
| print(hist_len) | |
| in_len=len(system_prompt+prompt)+hist_len | |
| print("\n#########"+in_len) | |
| if (in_len+tokens) > 8000: | |
| yield [(prompt,"Wait. I need to compress our Chat history...")] | |
| history=compress_history(history,client_choice,seed,temp,tokens,top_p,rep_p) | |
| yield [(prompt,"History has been compressed, processing request...")] | |
| generate_kwargs = dict( | |
| temperature=temp, | |
| max_new_tokens=tokens, | |
| top_p=top_p, | |
| repetition_penalty=rep_p, | |
| do_sample=True, | |
| seed=seed, | |
| ) | |
| #formatted_prompt=prompt | |
| formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history) | |
| stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False) | |
| output = "" | |
| for response in stream: | |
| output += response.token.text | |
| yield [(prompt,output)] | |
| history.append((prompt,output)) | |
| yield history | |
| def clear_fn(): | |
| return 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: | |
| gr.HTML("""<center><h1 style='font-size:xx-large;'>Google Gemma Models</h1><br><h3>running on Huggingface Inference Client</h3><br><h7>EXPERIMENTAL""") | |
| with gr.Row(): | |
| chat_a = gr.Chatbot(height=500) | |
| chat_b = gr.Chatbot(height=500) | |
| with gr.Row(): | |
| chat_c = gr.Chatbot(height=500) | |
| chat_d = 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],multiselect=True,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=3840,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) | |
| 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_a) | |
| #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,client_choice,seed,temp,tokens,top_p,rep_p],chat_b) | |
| #go=btn.click(check_rand,[rand,seed],seed).then(chat_inf,[sys_inp,inp,chat_b,client_choice,seed,temp,tokens,top_p,rep_p],chat_b) | |
| #stop_btn.click(None,None,None,cancels=[go,im_go,chat_sub]) | |
| #clear_btn.click(clear_fn,None,[inp,sys_inp,chat_b]) | |
| app.queue(default_concurrency_limit=10).launch() | |