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d9af773 | 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 | import gradio as gr
from huggingface_hub import InferenceClient
import torch
from transformers import pipeline
# Inference client setup
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
pipe = pipeline("text-generation", "microsoft/Phi-3-mini-4k-instruct", torch_dtype=torch.bfloat16, device_map="auto")
# Global flag to handle cancellation
stop_inference = False
# Default system message
DEFAULT_SYSTEM_MESSAGE = (
"You are a helpful chatbot who answers questions according to Occam's razor, "
"which suggests that the simplest explanation is usually the best one. Answer as concisely as possible. "
"DO NOT explain everything in 3-5 paragraphs. Only provide the single simplest possible answer or solution. "
"Ensure that the answer is still clearly explained to a user who does not understand, "
"but avoid long and drawn out answers to simple questions. Prioritize speed of answering."
)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens=512,
temperature=0.7,
top_p=0.95,
use_local_model=False,
):
global stop_inference
stop_inference = False # Reset cancellation flag
# Initialize history if it's None
if history is None:
history = []
# Use `system_message` from the state
if use_local_model:
# Local inference
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
for output in pipe(
messages,
max_new_tokens=max_tokens,
temperature=temperature,
do_sample=True,
top_p=top_p,
):
if stop_inference:
response = "Inference cancelled."
yield history + [(message, response)]
return
token = output['generated_text'][-1]['content']
response += token
yield history + [(message, response)] # Yield history + new response
else:
# API-based inference
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
for message_chunk in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
if stop_inference:
response = "Inference cancelled."
yield history + [(message, response)]
return
token = message_chunk.choices[0].delta.content
response += token
yield history + [(message, response)] # Yield history + new response
def cancel_inference():
global stop_inference
stop_inference = True
# Custom CSS for a fancy look
custom_css = """
#main-container {
background-color: #f0f0f0;
font-family: 'Arial', sans-serif;
}
.gradio-container {
max-width: 700px;
margin: 0 auto;
padding: 20px;
background: white;
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
border-radius: 10px;
}
.gr-button {
background-color: #4CAF50;
color: white;
border: none;
border-radius: 5px;
padding: 10px 20px;
cursor: pointer;
transition: background-color 0.3s ease;
}
.gr-button:hover {
background-color: #45a049;
}
.gr-slider input {
color: #4CAF50;
}
.gr-chat {
font-size: 16px;
}
#title {
text-align: center;
font-size: 2em;
margin-bottom: 20px;
color: #333;
}
"""
# Define the interface
with gr.Blocks(css=custom_css) as demo:
gr.Markdown("<h1 style='text-align: center;'>🪒 Occam's Chatbot 🪒</h1>")
gr.Markdown("Occam's Razor is the problem-solving principle that recommends searching for explanations constructed with the smallest possible set of elements.")
# Define a persistent state for the system message
system_message_state = gr.State(value=DEFAULT_SYSTEM_MESSAGE)
# Checkbox to toggle local model usage
use_local_model = gr.Checkbox(label="Use Local Model", value=False)
# Parameters for model control
max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
# Chat components
chat_history = gr.Chatbot(label="Chat")
user_input = gr.Textbox(show_label=False, placeholder="The simplest solution is often the best...")
cancel_button = gr.Button("Cancel Inference", variant="danger")
# Pass the `system_message_state` to the `respond` function
user_input.submit(respond, [user_input, chat_history, system_message_state, max_tokens, temperature, top_p, use_local_model], chat_history)
cancel_button.click(cancel_inference)
if __name__ == "__main__":
demo.launch(share=False) # Remove share=True because it's not supported on HF Spaces
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