app.py

import gradio as gr
import openai
import time
import re
import os

# Available models
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):
    """Creates an OpenAI client instance."""
    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):
    """Formats the chat history for the API call."""
    messages = [{"role": "system", "content": system_prompt}]
    for tup in chat_history:
        first_key = list(tup.keys())[0]  # First key
        last_key = list(tup.keys())[-1]  # Last key
        messages.append({"role": "user", "content": tup[first_key]})
        messages.append({"role": "assistant", "content": tup[last_key]})
    messages.append({"role": "user", "content": message})
    return messages

def respond(message, chat_history, model, system_prompt, thinking_budget, api_key):
    """Sends the message to the API and gets the response."""
    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
        return response, thinking_time
    except Exception as e:
        error_message = f"Error: {str(e)}"
        return error_message, time.time() - start_time

def parse_response(response):
    """Parses the response from the 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):
    """Generates the chatbot response."""
    # Use DEFAULT_SYSTEM_PROMPT inside the function
    system_prompt = DEFAULT_SYSTEM_PROMPT

    response, thinking_time = respond(message, history, model, system_prompt, thinking_budget, api_key)

    if response.startswith("Error:"):
        return history + [({"role": "system", "content": response},)], ""

    answer, reflection, steps = parse_response(response)

    messages = []
    messages.append({"role": "user", "content": message})

    formatted_steps = [f"Step {i}: {step}" for i, step in enumerate(steps, 1)]
    all_steps = "\n".join(formatted_steps) + f"\n\nReflection: {reflection}"

    messages.append({"role": "assistant", "content": all_steps, "metadata": {"title": f"Thinking Time: {thinking_time:.2f} sec"}})
    messages.append({"role": "assistant", "content": answer})

    return history + messages, ""

# Define the default 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?
"""

# Now, let's simplify the interface and remove unnecessary boxes like API Key and System Prompt

with gr.Blocks() as demo:
    # New header and description for D-LOGIC
    gr.Markdown("# D-LOGIC: Twój Inteligentny Asystent AI")
    gr.Markdown("""
    **D-LOGIC** to zaawansowany asystent AI stworzony przez Rafała Dembskiego. Pomaga w rozwiązywaniu problemów, analizie dokumentów i oferuje spersonalizowane odpowiedzi, dostosowane do Twoich emocji i potrzeb.
    """)

    with gr.Row():
        model = gr.Dropdown(choices=MODELS, label="Wybierz Model", value=MODELS[0])
        thinking_budget = gr.Slider(minimum=1, maximum=100, value=25, step=1, label="Budżet Myślenia", info="Maksymalna liczba kroków, które model może przemyśleć")

    chatbot = gr.Chatbot(label="Chat", show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel", type="messages")

    msg = gr.Textbox(label="Wpisz swoją wiadomość...", placeholder="Wprowadź swoją wiadomość...")

    submit_button = gr.Button("Wyślij")
    clear_button = gr.Button("Wyczyść Chat")

    clear_button.click(lambda: ([], ""), inputs=None, outputs=[chatbot, msg])

    # Submit messages by pressing Enter or clicking the Submit button
    msg.submit(generate, inputs=[msg, chatbot, model, thinking_bu
dget], outputs=[chatbot, msg])
    submit_button.click(generate, inputs=[msg, chatbot, model, thinking_budget], outputs=[chatbot, msg])

demo.launch(share=True, show_api=False)