app.py

import gradio as gr
import openai
import time
import re
import os
from PIL import Image
from transformers import LlavaProcessor, LlavaForConditionalGeneration, TextIteratorStreamer
from threading import Thread
import torch

# 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"

# Load image processing model
model_id = "llava-hf/llava-interleave-qwen-0.5b-hf"
processor = LlavaProcessor.from_pretrained(model_id)
model = LlavaForConditionalGeneration.from_pretrained(model_id)
model.to("cpu")

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 llava_image_processing(image, prompt):
    """Processes the image using the Llava model."""
    gr.Info("Analyzing image")
    image = Image.open(image).convert("RGB")
    formatted_prompt = f"<|im_start|>user <image>\n{prompt}<|im_end|><|im_start|>assistant"
    
    inputs = processor(formatted_prompt, image, return_tensors="pt")
    streamer = TextIteratorStreamer(processor, skip_prompt=True, **{"skip_special_tokens": True})
    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)
    
    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()
    
    buffer = ""
    for new_text in streamer:
        buffer += new_text
        yield buffer

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.
"""

# Updated interface with image analysis capability

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. Możesz także przesłać obraz do analizy!
    """)

    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")
    
    with gr.Row():
        msg = gr.Textbox(label="Wpisz swoją wiadomość...", placeholder="Wprowadź swoją wiadomość...")
        image_input = gr.File(label="Prześlij obraz do analizy (opcjonalnie)")

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

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

    def handle_message_or_image(message, image, chatbot, model, thinking_budget):
        if image:
            return llava_image_processing(image, message), ""
        else:
            return generate(message, chatbot, model, thinking_budget)

    # Submit messages by pressing Enter or clicking the Submit button
    submit_button.click(fn=handle_message_or_image, inputs=[msg, image_input, chatbot, model, thinking_budget], outputs=[chatbot, msg])

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