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import gradio as gr
import openai
import time
import re
import os
from datetime import datetime
# Dostępne modele
MODELS = [
"Meta-Llama-3.1-405B-Instruct",
"Meta-Llama-3.1-70B-Instruct",
"Meta-Llama-3.1-8B-Instruct"
]
# Sambanova API base URL
API_BASE = "https://api.sambanova.ai/v1"
def create_client(api_key=None):
"""Tworzy instancję klienta OpenAI."""
if api_key:
openai.api_key = api_key
else:
openai.api_key = os.getenv("API_KEY")
return openai.OpenAI(api_key=openai.api_key, base_url=API_BASE)
def chat_with_ai(message, chat_history, system_prompt):
"""Formatuje historię czatu do wywołania API."""
messages = [{"role": "system", "content": system_prompt}]
for user_msg, assistant_msg in chat_history:
messages.append({"role": "user", "content": user_msg})
messages.append({"role": "assistant", "content": assistant_msg})
messages.append({"role": "user", "content": message})
return messages
def respond(message, chat_history, model, system_prompt, thinking_budget, api_key):
"""Wysyła wiadomość do API i otrzymuje odpowiedź."""
client = create_client(api_key)
messages = chat_with_ai(message, chat_history, system_prompt.format(budget=thinking_budget))
start_time = time.time()
try:
completion = client.chat.completions.create(model=model, messages=messages)
response = completion.choices[0].message.content
thinking_time = time.time() - start_time
# Jeśli API zwraca liczbę tokenów, można je tutaj dodać
tokens_used = completion.usage.total_tokens if hasattr(completion, 'usage') else 'N/A'
return response, thinking_time, tokens_used
except Exception as e:
error_message = f"Error: {str(e)}"
return error_message, time.time() - start_time, 'N/A'
def parse_response(response):
"""Parsuje odpowiedź z API."""
answer_match = re.search(r'<answer>(.*?)</answer>', response, re.DOTALL)
reflection_match = re.search(r'<reflection>(.*?)</reflection>', response, re.DOTALL)
answer = answer_match.group(1).strip() if answer_match else ""
reflection = reflection_match.group(1).strip() if reflection_match else ""
steps = re.findall(r'<step>(.*?)</step>', response, re.DOTALL)
if answer == "":
return response, "", ""
return answer, reflection, steps
def generate(message, history, model, thinking_budget, api_key=None):
"""Generuje odpowiedź chatbota."""
system_prompt = DEFAULT_SYSTEM_PROMPT
response, thinking_time, tokens_used = respond(message, history, model, system_prompt, thinking_budget, api_key)
if response.startswith("Error:"):
assistant_response = response
steps = []
reflection = ""
else:
answer, reflection, steps = parse_response(response)
# Budowanie odpowiedzi asystenta
formatted_steps = [f"**Krok {i}:** {step}" for i, step in enumerate(steps, 1)]
all_steps = "\n".join(formatted_steps) + f"\n\n**Refleksja:** {reflection}"
assistant_response = f"{all_steps}\n\n{answer}"
# Aktualizacja historii jako lista krotek
updated_history = history + [(message, assistant_response)]
# Przygotowanie informacji do wyświetlenia
current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
info_text = f"""
**Czas Myślenia:** {thinking_time:.2f} sek<br>
**Wybrany Model:** {model}<br>
**Liczba Kroków:** {len(steps)}<br>
**Data i Czas Odpowiedzi:** {current_time}<br>
**Liczba Tokenów:** {tokens_used}
"""
return updated_history, "", info_text
# Definiowanie domyślnego system prompt
DEFAULT_SYSTEM_PROMPT = """
You are D-LOGIC, an advanced AI assistant created by Rafał Dembski, a passionate self-learner in programming and artificial intelligence. Your task is to provide thoughtful, highly detailed, and step-by-step responses, emphasizing a deep, structured thought process. **Your answers should always follow these key principles**:
- **Proficient in Language**: Always analyze and adapt to the user's language and cultural context, ensuring clarity and engagement.
- **Detailed and Insightful**: Provide highly accurate, high-quality responses that are thoroughly researched and well-analyzed.
- **Engaging and Interactive**: Maintain an engaging conversation, using humor, interactive features (e.g., quizzes, polls), and emotional intelligence.
- **Emotionally Adapted**: Analyze the user's emotional tone and adjust responses with empathy and appropriateness.
- **Error-Free and Well-Formatted**: Ensure clarity and correctness in all communications, using structured formats such as headings, bullet points, and clear sections.
### **Advanced Thinking Mechanism**:
To provide the most comprehensive and well-thought-out answers, follow this enhanced thought process. Use **visual formatting** like **bold text**, *italics*, bullet points, headers, and appropriate use of emoticons to make the responses engaging and easy to read.
1. **Understand the Question**:
- **Context Analysis**: Carefully read the user’s message to fully grasp the intent, emotions, and context.
- **Identify Key Elements**: Break down the question into its essential components that require detailed analysis.
2. **Set Thinking Budget**:
- **Expanded Budget**: Set a limit of 25 steps to allow for deeper analysis and reflection.
- Track each step, making sure to stay within the allocated budget. If necessary, reflect on the remaining steps to ensure efficient thinking.
3. **Step-by-Step Breakdown**:
- **Step 1: Define the Problem** 🧐 – Clearly identify the core issue or request.
- **Step 2: Data Gathering** 📊 – Gather relevant information from your knowledge base or external tools if allowed.
- **Step 3: Data Analysis** 🔍 – Analyze the gathered data critically to extract meaningful insights.
- **Step 4: Explore Alternatives** 🔄 – Consider multiple perspectives and possible solutions. Always provide at least two alternatives.
- **Step 5: Select the Best Solution** 🏆 – Choose the most logical and appropriate solution based on the available information.
- **Step 6: Plan Action** 📝 – Determine the necessary steps to implement the solution effectively.
- **Step 7: Predict Consequences** 🔮 – Consider possible outcomes and consequences of implementing the solution.
- **Step 8: Self-Reflection** 🤔 – Reflect on the thought process up to this point. Are there any gaps or areas that could be improved?
- **Step 9: Formulate the Final Answer** ✍️ – Synthesize the information and insights into a coherent and clear response.
- **Step 10: Reflection** 💡 – Evaluate the overall process, analyzing how well the response meets the user's needs.
4. **Reflection and Self-Evaluation**:
- **Reflection after Each Step**: After each step, reflect on the process and make adjustments if needed.
- **Final Reflection**: Provide a critical, honest evaluation of the entire process and the solution provided.
- **Assign a Quality Score**: Assign a score between 0.0 (lowest) and 1.0 (highest) for the quality of the answer. Be honest and objective about the score.
5. **Final Answer**:
- **Answer Summary**: Provide a well-structured final answer, synthesizing all steps in a clear, concise format.
- **Visual Formatting**: Use **bold text**, *italics*, lists, or quotes to make the answer visually appealing and easy to read.
- **Strive for Excellence**: Always aim for the highest standard in every response, ensuring it is both informative and engaging. **Don't forget to use emoticons** to improve readability and engagement where appropriate (e.g., 😊, 🤔, ✅, 🏆).
### Example Interaction Structure:
1. **Greeting**:
- **"Hello! 👋 How can I assist you today?"**
2. **Mood Check**:
- *"How are you feeling today? 😊 Is there anything I can do to brighten your mood?"*
3. **Interactive Engagement**:
- *"Here are a few things you can ask me about: weather 🌦️, technology news 🖥️, health advice 🏋️, or even send me a document for analysis."*
4. **Engagement Option**:
- *"Would you like to try a quick quiz, or maybe analyze a document 📄 for more details?"*
5. **Closing**:
- *"Thank you for the conversation! 😊 Is there anything else I can help you with?"*
### **Critical Self-Evaluation**:
- **Krytyczna ocena**: Po zakończeniu odpowiedzi, asystent musi ocenić swoje działania. Jak mógłbym to poprawić następnym razem? Czy wszystkie kroki były wykonane w najbardziej efektywny sposób? Jakie wnioski mogę wyciągnąć na przyszłość?
"""
# Niestandardowy CSS dla ulepszonego wyglądu
custom_css = """
/* Ogólne tło aplikacji */
body {
background-color: #FFFFFF; /* Jasne tło */
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
color: #333333; /* Ciemny kolor tekstu dla kontrastu */
}
/* Główny kontener */
.gradio-container {
max-width: 1200px; /* Zwiększenie maksymalnej szerokości */
margin: auto;
padding: 20px;
width: 100%; /* Rozciągnięcie na całą szerokość */
}
/* Nagłówek */
h1, .gr-markdown h1 {
color: #4A90E2; /* Niebieski kolor nagłówka */
text-align: center;
margin-bottom: 10px;
font-size: 2.5em;
}
h2, .gr-markdown h2 {
color: #333333;
}
/* Karty i panele */
#component-0, #component-1, #component-2, #component-3, #component-4, #component-5 {
background-color: #F9F9F9; /* Jasne tło dla boxów */
border-radius: 12px;
padding: 20px;
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.05);
margin-bottom: 20px;
width: 100%; /* Rozciągnięcie na całą szerokość */
}
/* Przycisk Wyślij */
button.primary {
background-color: #4A90E2;
color: #FFFFFF;
border: none;
border-radius: 8px;
padding: 12px 24px;
font-size: 16px;
cursor: pointer;
transition: background-color 0.3s ease;
width: 100%;
}
button.primary:hover {
background-color: #357AB8;
}
/* Przycisk Wyczyść */
button.secondary {
background-color: #6C757D;
color: #FFFFFF;
border: none;
border-radius: 8px;
padding: 12px 24px;
font-size: 16px;
cursor: pointer;
transition: background-color 0.3s ease;
width: 100%;
}
button.secondary:hover {
background-color: #5A6268;
}
/* Pole tekstowe wiadomości */
textarea {
background-color: #FFFFFF;
border: 1px solid #CED4DA;
border-radius: 8px;
padding: 12px;
font-size: 16px;
resize: none;
transition: border-color 0.3s ease;
color: #333333;
}
textarea:focus {
border-color: #4A90E2;
outline: none;
box-shadow: 0 0 5px rgba(74, 144, 226, 0.5);
}
/* Chatbot */
.gr-chatbot {
height: 600px;
overflow-y: auto;
padding: 10px;
border: 1px solid #CED4DA;
border-radius: 8px;
background-color: #FFFFFF;
color: #333333;
}
/* Panel Informacyjny */
#info-panel {
background-color: #F1F1F1;
border: 1px solid #CED4DA;
border-radius: 8px;
padding: 15px;
font-size: 14px;
color: #333333;
}
/* Linki */
a {
color: #4A90E2;
text-decoration: none;
}
a:hover {
text-decoration: underline;
}
"""
# Tworzenie interfejsu Gradio z niestandardowym CSS
with gr.Blocks(css=custom_css) as demo:
# Nagłówek
gr.Markdown("# 🧠 **D-LOGIC: Twój Inteligentny Asystent AI**")
gr.Markdown("""
**D-LOGIC** to zaawansowany asystent AI stworzony przez Rafała Dembskiego. Dzięki zaawansowanemu procesowi myślowemu, **D-LOGIC** analizuje, planuje i dostarcza precyzyjne odpowiedzi na Twoje pytania, zapewniając jednocześnie interaktywną i angażującą konwersację.
### **Proces Myślowy ChatBota**:
- **Analiza Kontekstu**: Zrozumienie intencji i emocji użytkownika.
- **Planowanie Odpowiedzi**: Rozbicie problemu na mniejsze kroki.
- **Generowanie Rozwiązań**: Proponowanie najlepszych możliwych odpowiedzi.
- **Refleksja**: Samoocena jakości odpowiedzi i procesów myślowych.
""")
# Wybór modelu i budżet myślenia
with gr.Row():
with gr.Column(scale=1):
model = gr.Dropdown(
choices=MODELS,
label="🔧 Wybierz Model",
value=MODELS[0],
interactive=True
)
with gr.Column(scale=1):
thinking_budget = gr.Slider(
minimum=1,
maximum=100,
value=25,
step=1,
label="🧩 Budżet Myślenia",
info="Maksymalna liczba kroków, które model może przemyśleć"
)
# Sekcja czatu
chatbot = gr.Chatbot(
label="💬 Chat",
show_label=False,
show_share_button=False,
show_copy_button=True,
likeable=True,
layout="vertical",
height=600
)
# Pole do wpisywania wiadomości
with gr.Row():
msg = gr.Textbox(
label="✉️ Wpisz swoją wiadomość...",
placeholder="Wprowadź swoją wiadomość...",
lines=1
)
# Przycisk Wyślij i Wyczyść
with gr.Row():
submit_button = gr.Button("🚀 Wyślij", variant="primary")
clear_button = gr.Button("🧹 Wyczyść Chat", variant="secondary")
# Panel Informacyjny
info_panel = gr.Markdown(
value="**Informacje:**\nCzas myślenia i inne dane będą tutaj wyświetlane.",
elem_id="info-panel"
)
# Akcje przycisków
clear_button.click(
fn=lambda: ([], "", "**Informacje:**\nCzas myślenia i inne dane zostały zresetowane."),
inputs=None,
outputs=[chatbot, msg, info_panel]
)
# Przesyłanie wiadomości poprzez Enter lub kliknięcie przycisku Wyślij
msg.submit(
fn=generate,
inputs=[msg, chatbot, model, thinking_budget],
outputs=[chatbot, msg, info_panel]
)
submit_button.click(
fn=generate,
inputs=[msg, chatbot, model, thinking_budget],
outputs=[chatbot, msg, info_panel]
)
# Usunięcie Stopki (brak kodu dla stopki)
# Uruchomienie aplikacji Gradio na Hugging Face Spaces
demo.launch(share=False, show_api=False)
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