import gradio as gr import random #from huggingface_hub import InferenceClient #import spaces import os os.environ["KERAS_BACKEND"] = "tensorflow" #"jax" "torch" os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"]="1.00" import keras_hub models = [ "hf://tatihden/gemma_mental_health_2b_it_en", "hf://tatihden/gemma_mental_health_2b_en", "hf://tatihden/gemma_mental_health_7b_it_en" ] clients = [] for model in models: clients.append(keras_hub.models.GemmaCausalLM.from_preset(model)) #from huggingface_hub import InferenceClient #clients = [] #for model in models: #clients.append(InferenceClient(model)) #@spaces.GPU def format_prompt(message, history): prompt = "" if history: for user_prompt, bot_response in history: prompt += f"user{user_prompt}" #prompt += f"model{bot_response}" prompt += f"user{message}model" return prompt def chat_inf(system_prompt, prompt, history, client_choice, seed, temp, tokens, top_p, rep_p): client = clients[int(client_choice) - 1] if not history: history = [] hist_len = 0 if history: hist_len = len(history) print(hist_len) #generate_kwargs = dict( #temperature=temp, #max_new_tokens=tokens, #top_p=top_p, #repetition_penalty=rep_p, #do_sample=True, #seed=seed, #) formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history) stream = client.generate(formatted_prompt) output = "" for response in stream: output+= response history.append((prompt, output)) yield history def clear_fn(): return None rand_val = random.randint(1, 1111111111111111) def check_rand(inp, val): if inp is 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(theme=gr.themes.Soft(),css=".gradio-container {background-color: rgb(187 247 208)}") as app: gr.HTML( """

CalmChat:A mental Health Conversational Agent

""") with gr.Group(): with gr.Row(): client_choice = gr.Dropdown(label="Models", type='index', choices=[c for c in models], value=models[0], interactive=True) chat_b = gr.Chatbot(height=500) with gr.Group(): with gr.Row(): 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=6400, minimum=0, maximum=8000, step=64, interactive=True, visible=True, info="The maximum number of tokens") with gr.Column(scale=1): with gr.Group(): 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.Group(): with gr.Row(): with gr.Column(scale=3): sys_inp = gr.Textbox(label="System Prompt (optional)") inp = gr.Textbox(label="Prompt") with gr.Row(): btn = gr.Button("Chat") stop_btn = gr.Button("Stop") clear_btn = gr.Button("Clear") 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, chat_sub]) clear_btn.click(clear_fn, None, [chat_b]) app.queue(default_concurrency_limit=10).launch()