Create app.py

app.py

@@ -0,0 +1,239 @@
+import os 
+from typing import Iterator
+import gradio as gr 
+from text_generation import Client
+
+repo_id = 
+HF_TOKEN = os.environ.get('HF_READ_TOKEN', False)
+EOS_STRING = '</s>'
+EOT_STRING = '<EOT>'
+
+def get_prompt(message, chat_history, system_prompt):
+    texts = [f'<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n']
+
+    do_strip = False
+    for user_input, response in chat_history:
+        user_input = user_input.strip() if do_strip else user_input
+        do_strip = True
+        texts.append(f"{user_input} [/INST\ {response.strip()} </s><s>[INST] ")
+    message = message.strip() if do_strip else message
+    texts.append(f"{message} [/INST]")
+    return ''.join(texts)
+
+def run(model_id, message, chat_history, system_prompt, max_new_tokens=1024, temperature=0.3, top_p=0.9, top_k=50):
+    API_URL = "https://api-inference.huggingface.co/models/" + model_id
+    client = Client(API_URL, header={'Authorization'L f"Bearer {HF_TOKEN}"})
+    generate_kwargs = dict(
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+        top_p=top_p,
+        top_k=top_k,
+        temperature=temperature
+    )
+    stream = client.generate_stream(prompt, **generate_kwargs)
+    output = ''
+    for response in stream:
+        if any([end_token in response.token.text for end_token in [EOS_STRING, EOT_STRING]]):
+            return output
+        else:
+            output += response.token.text
+        yield output
+    return output
+
+DEFAULT_SYSTEM_PROMPT = """
+    You are Jarvis. You are an AI assistant, you are moderately-polite and give only true information.
+    You carefully provide accurate, factual, thoughtful, nuanced answers, and are brilliant at reasoning. 
+    If you think there might not be a correct answer, you say so. Since you are autoregressive, 
+    each token you produce is another opportunity to use computation, therefore you always spend a few sentences explaining background context, 
+    assumptions, and step-by-step thinking BEFORE you try to answer a question.
+"""
+MAX_MAX_NEW_TOKENS = 4096
+DEFAULT_MAX_NEW_TOKENS = 256
+MAX_INPUT_TOKEN_LENGTH = 4000
+
+DESCRIPTION = "# He's just Jarvis. ;)"
+
+def clear_and_save_textbox(message): return '', message
+
+def display_output(message, history=[]):
+    history.append((message, ''))
+    return history
+
+def delete_prev_fn(history=[]):
+    try:
+        message, _ = history.pop()
+    except IndexError:
+        message = ''
+    return history, message or ''
+
+def generate(model_id, message, history_with_input, system_prompt, max_new_tokens, temperature, top_p, top_k):
+    if max_new_tokens > MAX_MAX_NEW_TOKENS:
+        raise ValueError
+
+    history = history_with_input[:-1]
+    generator = run(model_id, message, history, system_prompt, max_new_tokens, temperature, top_p, top_k)
+
+    try:
+        first_response = next(generator)
+        yield history + [(message, first_response)]
+    except StopIteration:
+        yield history + [(message, '')]
+    for response in generator:
+        yield history + [(message, response)]
+
+def process_example(model_id, message):
+    generator = generate(model)id, message, [], DEFAULT_SYSTEM_PROMPT, 1024, 1, 0.95, 50)
+    for x in generator:
+        pass
+    return '', x
+
+def check_input_token_length(message, chat_history, system_prompt):
+    input_token_length = len(message) + len(chat_history)
+    if input_token_length > MAX_INPUT_TOKEN_LENGTH:
+        raise gr.Error(f"The accumulated input is too long ({input_token_length} > {MAX_INPUT_TOKEN_LENGTH}). Client your chat history and try again.")
+
+with gr.Blocks(theme='Taithrah/Minimal') as demo:
+    gr.Markdown(DESCRIPTION)
+    with gr.Group():
+        chatbot = gr.Chatbot(label='Jarvis')
+        with gr.Row():
+            textbox = gr.Textbox(container=False, show_label=False, placeholder='Hey, Jarvis', scale=7)
+            model_id = gr.Dropdown(label='LLM',
+                                   choices=[
+                                       'mistralai/Mistral-7B-Instruct-v0.1', 
+                                       'HuggingFaceH4/zephyr-7b-beta', 
+                                       'meta-llama/Llama-2-7b-chat-hf'
+                                   ],
+                                  value='mistralai/Mistral-7B-Instruct-v0.1', scale=3)
+            submit_button = gr.Button('Submit', variant='primary', scale=1, min_width=0)
+
+        with gr.Row():
+            retry_button = gr.Button('Retry', variant='secondary')
+            undo_button = gr.Button('Undo', variant='secondary')
+            clear_button = gr.Button('Clear', variant='secondary')
+
+        saved_input = gr.State()
+
+        with gr.Accordion(label='Advanced Options', open=False):
+            system_prompt = gr.Textbox(label='System prompt',, value=DEFAULT_SYSTEM_PROMPT, lines=5, interactive=False)
+            max_new_tokens = gr.Slider(label='Max New Tokens', minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS)
+            temperature = gr.Slider(label='Temperatur', minimum=0.1, maximum=4.0, step=0.1, value=0.1)
+            top_p = gr.Slider(label='Top-P (nucleus sampling)', minimum=0.05, maximum=1.0, step=0.05, value=0.9)
+        top_k = gr.Slider(label='Top-K', minimum=1, maximum=1000, step=1, value=10)
+
+    textbox.submit(
+        fn=clear_and_save_textbox,
+        inputs=textbox,
+        outputs=[textbox, saved_input],
+        api_name=False,
+        queue=False,
+    ).then(
+        fn=display_input,
+        inputs=[saved_input, chatbot],
+        outputs=chatbot,
+        api_name=False,
+        queue=False,
+    ).then(
+        fn=check_input_token_length,
+        inputs=[saved_input, chatbot, system_prompt],
+        api_name=False,
+        queue=False,
+    ).success(
+        fn=generate,
+        inputs=[
+            model_id,
+            saved_input,
+            chatbot,
+            system_prompt,
+            max_new_tokens,
+            temperature,
+            top_p,
+            top_k,
+        ],
+        outputs=chatbot,
+        api_name=False,
+    )
+
+    button_event_preprocess = submit_button.click(
+        fn=clear_and_save_textbox,
+        inputs=textbox,
+        outputs=[textbox, saved_input],
+        api_name=False,
+        queue=False,
+    ).then(
+        fn=display_input,
+        inputs=[saved_input, chatbot],
+        outputs=chatbot,
+        api_name=False,
+        queue=False,
+    ).then(
+        fn=check_input_token_length,
+        inputs=[saved_input, chatbot, system_prompt],
+        api_name=False,
+        queue=False,
+    ).success(
+        fn=generate,
+        inputs=[
+            model_id,
+            saved_input,
+            chatbot,
+            system_prompt,
+            max_new_tokens,
+            temperature,
+            top_p,
+            top_k,
+        ],
+        outputs=chatbot,
+        api_name=False,
+    )
+
+    retry_button.click(
+        fn=delete_prev_fn,
+        inputs=chatbot,
+        outputs=[chatbot, saved_input],
+        api_name=False,
+        queue=False,
+    ).then(
+        fn=display_input,
+        inputs=[saved_input, chatbot],
+        outputs=chatbot,
+        api_name=False,
+        queue=False,
+    ).then(
+        fn=generate,
+        inputs=[
+            model_id,
+            saved_input,
+            chatbot,
+            system_prompt,
+            max_new_tokens,
+            temperature,
+            top_p,
+            top_k,
+        ],
+        outputs=chatbot,
+        api_name=False,
+    )
+
+    undo_button.click(
+        fn=delete_prev_fn,
+        inputs=chatbot,
+        outputs=[chatbot, saved_input],
+        api_name=False,
+        queue=False,
+    ).then(
+        fn=lambda x: x,
+        inputs=[saved_input],
+        outputs=textbox,
+        api_name=False,
+        queue=False,
+    )
+
+    clear_button.click(
+        fn=lambda: ([], ''),
+        outputs=[chatbot, saved_input],
+        queue=False,
+        api_name=False,
+    )
+
+demo.queue(max_size=32).launch(show_api=False)