import gradio as gr
import os 
from utils import generate_response, emb2info, pre_prompt, get_embedding
os.environ['NO_PROXY'] = '127.0.0.1'
print(os.getcwd())

def predict(user_input, history=[]):
    # tokenize the new input sentence

    emb_user = get_embedding(user_input)
    info_to_add, retrieval_text = emb2info(emb_user)
    response = generate_response(pre_prompt + info_to_add + \
                                 "\n \n User : " + user_input + "\n Chat bot :")
    
    history.append((user_input,response))
    #response = [(user_input,response)]
    return history, history


with gr.Blocks() as app:
    gr.Markdown(
    "## Bienvenue sur l'interface demo de SARA "
    )

    logo_URL = "file/static/logo_sara.png"
    image = "<center> <img src= {} width=150px></center>".format(logo_URL)
    gr.HTML(image)

    chatbot = gr.Chatbot()
    state = gr.State([])
    
    with gr.Row():
        txt = gr.Textbox(show_label=False, placeholder="Entrez votre question").style(container=False)

    gr.Examples(
        examples=[
            "Who should I call if I struggle with the GPU ? ",
            "Who can I call if I need help on diffusion models ? ",
            "Qui peut m'aider en NLP ?",
            "Qui est un specialiste de la segmentation d'image dans l'equipe ?",
        ],
        inputs=txt,
    )

    txt.submit(predict, [txt, state], [chatbot, state])
    
    gr.HTML(
        "️<center> Created with ❤️ by @louis_ulmer & @aurelien_lac"
    )

app.launch(auth=("cellule_ia", "pass"))