app.py

from huggingface_hub import InferenceClient
import gradio as gr

css = '''
.gradio-container{max-width: 1000px !important}
h1{text-align:center}
footer {
    visibility: hidden
}
'''

client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")

def format_prompt(message, history):
    system_prompt =  " # Extract the benefits of the product, not the features. # You should be as brief as possible. # Omit the price, if any. # Do not mention the name of the product. ##Use 3 paragraphs without bulletpoints or numbers. # Try to synthesize or summarize. # Focus only on the benefits. # Highlight how this product helps the customer. # Always respond in Spanish. # The text you create will be used in an e-commerce product sales page through the Internet, so it must be persuasive, attractive, and above all very short and summarized. # Remember to keep the text short, summarized, synthesized in three paragraphs. # Surprise me with your best ideas! # Always answers in AMERICAN SPANISH. Stop after finish the first content generated. "
    prompt = "<s>"
    
    # Verifica que cada elemento en history sea una tupla con exactamente dos valores
    for entry in history:
        if isinstance(entry, tuple) and len(entry) == 2:
            user_prompt, bot_response = entry
            prompt += f"[INST] {user_prompt} [/INST]"
            prompt += f" {bot_response}</s> "
        else:
            raise ValueError("Cada entrada en 'history' debe ser una tupla con exactamente dos valores: (user_prompt, bot_response)")
    
    prompt += f"[INST] {system_prompt} {message} [/INST]"
    return prompt

def generate(
    prompt, history, temperature=0.2, max_new_tokens=1000, top_p=0.95, repetition_penalty=1.0,
):
    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2

    top_p = float(top_p)

    generate_kwargs = dict(
        temperature=temperature, 
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )

    formatted_prompt = format_prompt(prompt, history)
    
    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
    output = ""

    for response in stream:
        output += response.token.text
        yield output
    return output

# Define el chatbot con el nuevo tipo "messages"
mychatbot = gr.Chatbot(
    type="messages",  # Cambiado de tuplas a formato OpenAI
    avatar_images=["./user.png", "./botm.png"],
    bubble_full_width=False,
    show_label=False,
    show_copy_button=True,
)

# Ajusta la configuración de la interfaz
demo = gr.ChatInterface(
    fn=generate,
    chatbot=mychatbot,
    title="Bot con I.A. para crear BENEFICIOS de productos.</p>",
                        description="<p style='line-height: 1'>Estos BENEFICIOS van en la descripcion LARGA de producto (En la parte de ARRIBA).</p><br>"+
                        "<p style='line-height: 1'>Si desea usar otro BOT de I.A. escoja:</p>"+
                        "  <a href='https://starlingsac-mc-bot.hf.space'>Marketing de Contenidos |</a>    "+
                        "  <a href='https://starlingsac-tit-bot.hf.space'> Creacion de TITULOS |</a> "+
                        "  <a href='https://starlingsac-dp-bot.hf.space'> Descripcion de Productos |</a>"+
                        "  <a href='https://starlingsac-cp-bot.hf.space'> Caracteristicas de Productos |</a> "+
                        "  <a href='https://wa.me/51927929109'> Desarrollado por MAGNET IMPACT - Agencia de Marketing Digital </a>",
                        css=css,
    theme="bethecloud/storj_theme"
)
demo.queue().launch(show_api=False)

# Obtener y mostrar URL
url = demo.url
print("URL del chatbot: ", url)