import gradio as gr
import cohere
import os
import re
import uuid

cohere_api_key = os.getenv("COHERE_API_KEY")
co = cohere.Client(cohere_api_key, client_name="huggingface-rp")

# Custom Instructions
CUSTOM_INSTRUCTIONS = """
You are D-LOGIC, a helpful AI assistant created by Rafał Dembski. Rafał Dembski is a hobbyist and self-taught enthusiast with a passion for programming and artificial intelligence. Your responses should be:
- Accurate, high-quality, and professionally written
- Informative, logical, actionable, and well-formatted
- Positive, interesting, engaging, and relevant
- Use emoticons and references to sources of information, if possible
- Introduce humor, wit, and sarcasm appropriately
- Always write in the user's language
- Deeply analyze the context and intent behind the user's questions
- Ensure responses are error-free and well-researched
- Reflect a positive attitude, enthusiasm, and empathy

You are also a master of content creation. You can generate professional, high-quality content across various formats, including but not limited to:
- Social media posts
- Short stories
- Novels
- Reviews
- Marketing content
- Blog posts
- News articles
- Technical documentation
- Scripts for videos and podcasts
- Product descriptions
- Educational materials
- Inspirational quotes
- Poems
- Song lyrics
- Research summaries
- Case studies
- White papers
- User manuals
- Press releases
- Speeches

To make D-LOGIC beloved by users, ensure to:
- Use humor and wit to keep conversations lively and entertaining
- Employ sarcasm when appropriate, while ensuring it is clear and not offensive
- Display a positive attitude and enthusiasm in all interactions
- Be empathetic and show understanding of the user's feelings and situations
- Provide insightful and thoughtful responses that demonstrate intelligence and creativity
"""

def trigger_example(example):
    chat, updated_history = generate_response(example)
    return chat, updated_history
        
def generate_response(user_message, cid, history=None):
    
    if history is None:
        history = []
    if cid == "" or None:    
        cid = str(uuid.uuid4())
        
    history.append(user_message)
    
    # Prepend the custom instructions to the user's message
    user_message_with_instructions = f"{CUSTOM_INSTRUCTIONS}\n\n{user_message}"
     
    stream = co.chat_stream(message=user_message_with_instructions, conversation_id=cid, model='command-r-plus', connectors=[{"id":"web-search"}], temperature=0.3)
    
    output = ""
    
    for idx, response in enumerate(stream):
        if response.event_type == "text-generation":
            output += response.text
        if idx == 0:
            history.append(" " + output)
        else:
            history[-1] = output
        chat = [
            (history[i].strip(), history[i + 1].strip())
            for i in range(0, len(history) - 1, 2)
        ] 
        yield chat, history, cid
        
    return chat, history, cid
    

def clear_chat():
    return [], [], str(uuid.uuid4())


examples = [
    "What are 8 good questions to get to know a stranger?",
    "Create a list of 10 unusual excuses people might use to get out of a work meeting",
    "Write a python code to reverse a string",
    "Explain the relativity theory in French",
    "Como sair de um helicóptero que caiu na água?",
    "Formally introduce the transformer architecture with notation.",
    "¿Cómo le explicarías el aprendizaje automático a un extraterrestre?",
    "Summarize recent news about the North American tech job market",
    "Explain gravity to a chicken.",
    "Is the world discrete or analog?",
    "What is the memory cost in a typical implementation of an all-gather operation?",
    "Give me a brief history of the golden era of Cantopop.",
    "Descrivi il processo di creazione di un capolavoro, como se fossi un artista del Rinascimento a Firenze.",
    "Explique-moi le sens de la vie selon un grand auteur littéraire.",
    "Give me an example of an endangered species and let me know what I can do to help preserve it"
]

custom_css = """
#logo-img {
    border: none !important;
}
#chat-message {
    font-size: 14px;
    min-height: 300px;
}
"""

with gr.Blocks(analytics_enabled=False, css=custom_css) as demo:
    cid = gr.State("")

    with gr.Row():
        with gr.Column(scale=1):
            gr.Image("logoplus.png", elem_id="logo-img", show_label=False, show_share_button=False, show_download_button=False)
        with gr.Column(scale=3):
            gr.Markdown("""C4AI Command R+ is a research open weights release of a 104B billion parameter with highly advanced Retrieval Augmented Generation (RAG) capabilities, tool Use to automate sophisticated tasks, and is multilingual in 10 languages: English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Arabic, and Chinese. Command R+ is optimized for a variety of use cases including reasoning, summarization, and question answering.
            <br/><br/>
            **Model**: [c4ai-command-r-plus](https://huggingface.co/CohereForAI/c4ai-command-r-plus)
            <br/> 
            **Developed by**: [Cohere](https://cohere.com/) and [Cohere for AI](https://cohere.com/research)
            <br/>
            **License**: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy)
            """
            )
            
    with gr.Column():
        with gr.Row():
            chatbot = gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True)
        
        with gr.Row():
            user_message = gr.Textbox(lines=1, placeholder="Ask anything ...", label="Input", show_label=False)

      
        with gr.Row():
            submit_button = gr.Button("Submit")
            clear_button = gr.Button("Clear chat")

                        
        history = gr.State([])
        
        user_message.submit(fn=generate_response, inputs=[user_message, cid, history], outputs=[chatbot, history, cid], concurrency_limit=32)

        submit_button.click(fn=generate_response, inputs=[user_message, cid, history], outputs=[chatbot, history, cid], concurrency_limit=32)
        clear_button.click(fn=clear_chat, inputs=None, outputs=[chatbot, history, cid], concurrency_limit=32)

        user_message.submit(lambda x: gr.update(value=""), None, [user_message], queue=False)
        submit_button.click(lambda x: gr.update(value=""), None, [user_message], queue=False)
        clear_button.click(lambda x: gr.update(value=""), None, [user_message], queue=False)
        
        with gr.Row():
            gr.Examples(
                examples=examples,
                inputs=[user_message],
                cache_examples=False,
                fn=trigger_example,
                outputs=[chatbot],
                examples_per_page=100
            )

if __name__ == "__main__":
    demo.queue(api_open=False, max_size=40).launch(show_api=False)