Create app.py

app.py

@@ -0,0 +1,137 @@
+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]),
+]'''
+
+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],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)
+            
+
+
+    #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()