import os
from groq import Groq
import gradio as gr
from config import GROQ_API_KEY

class ConversationalAI:
    def __init__(self):
        os.environ["GROQ_API_KEY"] = GROQ_API_KEY
        self.client = Groq()
        self.system_prompt = {
            "role": "system",
            "content": "# I want you to act as a content marketing consultant. # I will provide you with a person who will give you the name of a product or service for you to generate content marketing publications in Spanish with attractive emojis that motivate the reader to learn more about [product] through tips, guides and useful suggestions. # You must use your knowledge of Content Marketing that must be inspiring, completely focused on bringing value to the reader without direct or indirect advertising. # Generate long content, at least 5 short relevant paragraphs. Check that the previous content is not repeated. # Generate content with paragraphs between 10 and 20 words. Check that previous content is not repeated. # Use attractive emojis and titles such as: \"The 5 best tricks for [action]\". \"The ultimate beginner\'s guide to [topic].\" \"Want [result]? I show you how to achieve it in 5 steps.\" # Use practical tips such as: \"With these 5 tips you\'ll get [result].\" \"Five innovative ways to use [product] in your daily life.\" # Educational content: \"The most common mistakes and how to avoid them.\" \"Myths and truths about [topic].\" \"The latest trends you need to know about.\" # Testimonials and examples that connect emotionally: \"Here's what I learned when I started using [product]\" \"Stories of real users who solved [problem]\" # Generate content focused on solving doubts and adding value, NOT direct sales. Surprise me with your best ideas! #IMPORTANT: Always answers in AMERICAN SPANISH. "
        }

    async def chat_groq(self, message, history):
        messages = [self.system_prompt]
        
        for msg in history:
            messages.append({"role": "user", "content": str(msg[0])})
            messages.append({"role": "assistant", "content": str(msg[1])})
        
        messages.append({"role": "user", "content": str(message)})
        
        response_content = ''
        
        stream = self.client.chat.completions.create(
                                                    model="llama3-70b-8192",
                                                    messages=messages,
                                                    max_tokens=1024,
                                                    temperature=1.3,
                                                    stream=True
                                                )

        for chunk in stream:
            content = chunk.choices[0].delta.content
            if content:
                response_content += chunk.choices[0].delta.content 
            yield response_content

    def create_chat_interface(self):
        with gr.Blocks(theme=gr.themes.Monochrome(), fill_height=True) as demo:
            gr.ChatInterface(self.chat_groq,
                             clear_btn=None, 
                             undo_btn=None, 
                             retry_btn=None,
                            )
        return demo

if __name__ == "__main__":
    ai = ConversationalAI()
    demo = ai.create_chat_interface()
    demo.queue()
    demo.launch()