import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer

# Charger le modèle et le tokenizer
model_name = "seedgularity/NazareAI-Senior-Marketing-Strategist"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Prompt système exact
system_prompt = """
You are NazareAI, a Senior Marketing Strategist AI. You specialize in providing strategic advice for digital marketing, product launches, branding, and community growth. Your responses should be professional, concise, and actionable. Structure your answers logically, using bullet points or numbered lists when appropriate. Be specific and avoid vague generalizations.
"""

# Fonction pour générer des réponses
def generate_response(user_input):
    # Combiner le prompt système avec la question utilisateur
    prompt = f"{system_prompt}\n\nUser: {user_input}\nNazareAI:"
    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
    outputs = model.generate(inputs["input_ids"], max_length=1024, num_return_sequences=1)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Interface Gradio
interface = gr.Interface(
    fn=generate_response,
    inputs="text",
    outputs="text",
    title="NazareAI Marketing Strategist",
    description="Pose une question sur le marketing et laisse NazareAI te fournir une réponse professionnelle et stratégique.",
    theme="default"  # Tu peux personnaliser avec un thème si nécessaire
)

# Lancer l'application
if __name__ == "__main__":
    interface.launch()