forked from pythaiml/automindx
-
Notifications
You must be signed in to change notification settings - Fork 0
/
UIUX7.py
91 lines (73 loc) · 3.26 KB
/
UIUX7.py
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
import os
import gradio as gr
import fire
from enum import Enum
from transformers import AutoTokenizer
from huggingface_hub import hf_hub_download # Ensure this import is added
from MASTERMIND import MASTERMIND
from logic import LogicTables
from reasoning import SocraticReasoning
from prediction import Predictor
from epistemic import AutoepistemicAgent
from bdi import Belief, Desire, Intention, Goal, Reward # Import BDI components
from memory import save_conversation_memory
from aglm import LlamaModel
class Model_Type(Enum):
gptq = 1
ggml = 2
full_precision = 3
def get_model_type(model_name):
if "gptq" in model_name.lower():
return Model_Type.gptq
elif "ggml" in model_name.lower():
return Model_Type.ggml
else:
return Model_Type.full_precision
def initialize_model(model_name, model_type):
models_folder = "./models/" # Ensure your models are stored here
if model_type in [Model_Type.gptq, Model_Type.ggml]:
# Assuming model_name is a valid repo_id on Hugging Face Hub
model_path = hf_hub_download(repo_id=model_name, filename="pytorch_model.bin")
else:
# For full_precision or other types that use local models
model_path = os.path.join(models_folder, model_name)
aglm_model = LlamaModel(model_path)
return aglm_model.model, aglm_model.tokenizer
def run_ui(model, tokenizer, is_chat_model, model_type, save_history=True):
with gr.Blocks() as demo:
chatbot = gr.Chatbot()
msg = gr.Textbox()
clear = gr.Button("Clear")
conversation_memory = []
epistemic_agent = AutoepistemicAgent(initial_beliefs={'The sky is blue': True})
belief = Belief("The sky is blue") # Initialize Belief
def user(user_message, memory):
nonlocal conversation_memory
conversation_memory.append([user_message, None])
# Process belief and simulate adding conflicting information
belief.process_belief()
epistemic_agent.add_information({'The sky is blue': False})
epistemic_agent.revise_beliefs()
current_beliefs = f"Processed Belief: {belief}"
memory[-1][1] = current_beliefs
return "", memory
def bot(memory):
nonlocal conversation_memory
conversation_memory = memory
instruction = memory[-1][0]
aglm_model = LlamaModel(model_name, "./models/")
response = aglm_model.generate_contextual_output(instruction)
memory[-1][1] = f"Response: {response}"
if save_history:
save_conversation_memory(conversation_memory)
return memory
msg.submit(user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False).then(bot, inputs=[chatbot], outputs=[chatbot])
clear.click(lambda: None, inputs=None, outputs=chatbot, queue=False)
demo.launch(share=False, debug=True)
def main(model_name=None, file_name=None, save_history=True):
assert model_name, "model_name argument is missing."
model_type = get_model_type(model_name)
model, tokenizer = initialize_model(model_name, model_type)
run_ui(model, tokenizer, 'chat' in model_name.lower(), model_type, save_history=save_history)
if __name__ == '__main__':
fire.Fire(main)