Create app.py

app.py

@@ -0,0 +1,166 @@
+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]),
+]
+
+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 = ""
+    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,client_choice,seed,temp,tokens,top_p,rep_p,chat_mem,cust_p):
+    #token max=8192
+    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)
+        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:
+    memory=gr.State()
+    gr.HTML("""<center><h1 style='font-size:xx-large;'>Google Gemma Models</h1><br><h3>running on Huggingface Inference Client</h3><br><h7>EXPERIMENTAL""")
+    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])
+    app.load(load_models,client_choice,[chat_b])
+    
+    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,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,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()