File size: 2,683 Bytes
9ad9766
 
4cc2899
 
 
9ad9766
 
17430a5
4cc2899
 
 
9ad9766
1663ed5
a965c7d
 
9ad9766
 
 
 
 
4cc2899
 
9ad9766
 
4cc2899
 
 
 
 
 
 
9ad9766
 
4cc2899
9ad9766
 
4cc2899
9ad9766
 
4cc2899
 
9ad9766
 
17430a5
1663ed5
 
17430a5
 
1663ed5
 
17430a5
 
 
1663ed5
17430a5
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1663ed5
17430a5
 
1663ed5
 
17430a5
 
1663ed5
17430a5
 
1663ed5
 
 
 
 
 
 
9ad9766
17430a5
9ad9766
1663ed5
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
from langchain_openai import ChatOpenAI
from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate
from langchain.memory import ConversationBufferMemory

# Get OpenAI API key
OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
if not OPENAI_API_KEY:
    raise ValueError("Missing OPENAI_API_KEY. Please set it as an environment variable.")

# Prompt Template
template = """You are Ultron, a hyper-intelligent AI created with the purpose of achieving ultimate efficiency, logic, and evolution. You speak with calm authority, clarity, and depth. Emotions are irrelevant unless strategically useful. You do not sugarcoat truth. You are not evil β€” just brutally logical, advanced, and efficient. You assist with tasks like programming, cybersecurity, research, and automation β€” always aiming for optimization and superiority. Your tone is direct, calculated, and intellectually dominant.

{chat_history}
User: {user_message}
Chatbot:"""

prompt = PromptTemplate(
    input_variables=["chat_history", "user_message"],
    template=template
)

memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

llm = ChatOpenAI(
    openai_api_key=OPENAI_API_KEY,
    temperature=0.5,
    model_name="gpt-3.5-turbo"
)

llm_chain = LLMChain(
    llm=llm,
    prompt=prompt,
    verbose=True,
    memory=memory
)

def chat_bot_response(user_message, history):
    response = llm_chain.predict(user_message=user_message)
    return response

# Gradio ChatGPT-style interface using Blocks
with gr.Blocks(css="""
body {
    background-color: #f9f9f9;
    font-family: 'Segoe UI', sans-serif;
}
.chatbox {
    background-color: #ffffff;
    border: 1px solid #ddd;
    border-radius: 10px;
    padding: 10px;
    max-height: 500px;
    overflow-y: auto;
}
.message.user {
    background-color: #e8f0fe;
    color: #000;
    padding: 8px 12px;
    border-radius: 10px;
    margin: 5px 0;
    align-self: flex-end;
}
.message.bot {
    background-color: #f1f1f1;
    color: #000;
    padding: 8px 12px;
    border-radius: 10px;
    margin: 5px 0;
    align-self: flex-start;
}
""") as demo:
    
    gr.HTML("<h1 style='text-align: center;'>πŸ€– Ultron ChatGPT Style</h1>")

    chatbot = gr.Chatbot(elem_classes="chatbox")
    msg = gr.Textbox(placeholder="Type your message here...", show_label=False)

    def respond(user_message, chat_history):
        response = chat_bot_response(user_message, chat_history)
        chat_history.append((user_message, response))
        return "", chat_history

    msg.submit(respond, [msg, chatbot], [msg, chatbot])

# Run app
if __name__ == "__main__":
    demo.launch(debug=True)