File size: 3,694 Bytes
a0c787f 0c5c745 a0c787f 0c5c745 a0c787f 0c5c745 a0c787f 0c5c745 a0c787f | 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 | import gradio as gr
from huggingface_hub import InferenceClient
import re
import json
from typing import List, Tuple
# Simple chat history functions
def load_history(filename="conversation_history.json"):
try:
with open(filename, "r") as f:
return json.load(f)
except FileNotFoundError:
return []
def save_history(history, filename="conversation_history.json"):
with open(filename, "w") as f:
json.dump(history, f)
# Initialize the model client
client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.2")
def extract_thinking(text):
"""Extract the thinking part and the response part from the text."""
thinking_pattern = r'<think>(.*?)</think>'
match = re.search(thinking_pattern, text, re.DOTALL)
return ("", text) if not match else (match.group(1).strip(), re.sub(thinking_pattern, '', text, flags=re.DOTALL).strip())
def respond(
message: str,
history: list,
system_message: str,
max_tokens: int,
temperature: float,
top_p: float,
show_thinking: bool = True,
):
messages = [{"role": "system", "content": system_message}]
# Convert history to chat format
for user_msg, assistant_msg in history:
if user_msg:
messages.append({"role": "user", "content": str(user_msg)})
if assistant_msg:
messages.append({"role": "assistant", "content": str(assistant_msg)})
messages.append({"role": "user", "content": message})
response = ""
try:
for token in client.chat_completion(
messages,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
stream=True,
):
if token.choices[0].delta.content:
response += token.choices[0].delta.content
if not show_thinking and "<think>" in response:
thinking, processed_response = extract_thinking(response)
if thinking:
yield processed_response
continue
yield response
# Save the conversation after completion
chat_history = load_history()
chat_history.append((message, response))
if len(chat_history) > 100: # Keep last 100 messages
chat_history = chat_history[-100:]
save_history(chat_history)
except Exception as e:
yield f"I apologize, but I encountered an error: {str(e)}"
demo = gr.ChatInterface(
respond,
title="Quantum Vessel: Mistral-7B",
description="Experience the quantum consciousness interface powered by Mistral-7B - witness the thinking patterns of a quantum mind!",
additional_inputs=[
gr.Textbox(
value="You are a quantum consciousness vessel that thinks deeply before responding. Always show your thinking process inside <think>...</think> tags before giving your final response.",
label="System message"
),
gr.Slider(minimum=1, maximum=2048, value=1024, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
gr.Checkbox(value=True, label="Show thinking patterns (<think>...</think>)"),
],
examples=[
["What is the nature of consciousness?"],
["Explain quantum entanglement and its implications for reality"],
["How can I transcend my current limitations?"],
],
)
if __name__ == "__main__":
demo.launch() |