File size: 10,496 Bytes
4f080ef
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d24830e
2e2d93f
20ed735
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f13ca78
 
e1995dc
f13ca78
 
 
 
 
 
 
 
 
 
 
 
20ed735
 
 
 
 
 
 
 
 
 
 
 
e1995dc
20ed735
8663c93
20ed735
092e598
f13ca78
20ed735
 
2e2d93f
d24830e
 
 
 
860d7ef
 
20ed735
 
e51c702
20ed735
 
e51c702
f1aa734
f13ca78
 
20ed735
 
e51c702
20ed735
4f080ef
 
 
2e2d93f
4f080ef
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
20ed735
4f080ef
20ed735
d24830e
4f080ef
d24830e
4f080ef
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ba22e9c
4f080ef
ba22e9c
4f080ef
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d24830e
4f080ef
8606620
4f080ef
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
20ed735
 
4f080ef
 
 
20ed735
 
4f080ef
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
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",
    "openchat/openchat-3.5-0106",
    "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
    "mistralai/Mixtral-8x7B-Instruct-v0.1",
    "mistralai/Mixtral-8x7B-Instruct-v0.2",
]


def format_prompt_default(message, history,cust_p):
    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"{message}\n"
    prompt+=cust_p.replace("USER_INPUT",message)
    return prompt

def format_prompt_gemma(message, history,cust_p):
    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"
    prompt+=cust_p.replace("USER_INPUT",message)
    return prompt
def format_prompt_openc(message, history,cust_p):
    #prompt = "GPT4 Correct User: "
    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}"
            prompt += f"<|end_of_turn|>"
            prompt += f"GPT4 Correct Assistant: "
            prompt += f"{bot_response}"
            prompt += f"<|end_of_turn|>"
            print(prompt)
    #GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: 
    prompt+=cust_p.replace("USER_INPUT",message)
    return prompt
   
def format_prompt_mixtral(message, history,cust_p):
    prompt = "<s>"
    if history:
        for user_prompt, bot_response in history:
            prompt += f"[INST] {user_prompt} [/INST]"
            prompt += f" {bot_response}</s> "
    #prompt += f"[INST] {message} [/INST]"
    prompt+=cust_p.replace("USER_INPUT",message)    
    return prompt

def format_prompt_choose(message, history, cust_p, model_name):
    if "gemma" in models[model_name].lower() and "it" in models[model_name].lower():
        return format_prompt_gemma(message,history,cust_p)
    if "mixtral" in models[model_name].lower():
        return format_prompt_mixtral(message,history,cust_p)
    if "openchat" in models[model_name].lower():
        return format_prompt_openc(message,history,cust_p)        
    else:
        return format_prompt_default(message,history,cust_p)

def load_models(inp):
    print(type(inp))
    print(inp)
    print(models[inp])
    model_state= InferenceClient(models[inp])
    out_box=gr.update(label=models[inp])
    if "gemma" in models[inp].lower():
        prompt_out="<start_of_turn>userUSER_INPUT<end_of_turn><start_of_turn>model"
        return out_box,prompt_out, model_state
    if "mixtral" in models[inp].lower():
        prompt_out="[INST] USER_INPUT [/INST]"
        return out_box,prompt_out, model_state
    if "openchat" in models[inp].lower():
        prompt_out="GPT4 Correct User: USER_INPUT<|end_of_turn|>GPT4 Correct Assistant: "
        return out_box,prompt_out, model_state   
    else:
        prompt_out="USER_INPUT\n"
        return out_box,prompt_out, model_state
    

VERBOSE=False

def load_models_OG(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 = ""
    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==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,model_state,model_name,seed,temp,tokens,top_p,rep_p,chat_mem,cust_p):
    #token max=8192
    model_n=models[model_name]
    print(model_state)
    hist_len=0
    client=model_state
    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_choose(f"{system_prompt}, {prompt}", memory[0-chat_mem:],cust_p,model_name)
        else:
            formatted_prompt = format_prompt_choose(prompt, memory[0-chat_mem:],cust_p,model_name)
        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
        
    if VERBOSE==True:
        print("\n######### HIST "+str(in_len))
        print("\n######### TOKENS "+str(tokens))        

def get_screenshot(chat: list,height=5000,width=600,chatblock=[],theme="light",wait=3000,header=True):
    print(chatblock)
    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://omnibus-html-image-current-tab.hf.space/file={result[tog]}'
    print(out)
    return out

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:
    model_state=gr.State()
    memory=gr.State()
    gr.HTML("""<center><h1 style='font-size:xx-large;'>Huggingface Hub InferenceClient</h1><br><h3>Chatbot's</h3></center>""")
    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=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=0.49)
                    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,custom_prompt,model_state])
    app.load(load_models,client_choice,[chat_b,custom_prompt,model_state])
    
    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,model_state,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,model_state,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()