app.py

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)