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import gradio as gr |
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import os |
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from utils import generate_response, emb2info, pre_prompt, get_embedding |
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os.environ['NO_PROXY'] = '127.0.0.1' |
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print(os.getcwd()) |
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def predict(user_input, history=[]): |
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emb_user = get_embedding(user_input) |
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info_to_add, retrieval_text = emb2info(emb_user) |
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response = generate_response(pre_prompt + info_to_add + \ |
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"\n \n User : " + user_input + "\n Chat bot :") |
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history.append((user_input,response)) |
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return history, history |
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with gr.Blocks() as app: |
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gr.Markdown( |
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"## Bienvenue sur l'interface demo de SARA " |
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) |
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logo_URL = "file/static/logo_sara.png" |
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image = "<center> <img src= {} width=150px></center>".format(logo_URL) |
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gr.HTML(image) |
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chatbot = gr.Chatbot() |
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state = gr.State([]) |
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with gr.Row(): |
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txt = gr.Textbox(show_label=False, placeholder="Entrez votre question").style(container=False) |
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gr.Examples( |
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examples=[ |
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"Who should I call if I struggle with the GPU ? ", |
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"Who can I call if I need help on diffusion models ? ", |
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"Qui peut m'aider en NLP ?", |
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"Qui est un specialiste de la segmentation d'image dans l'equipe ?", |
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], |
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inputs=txt, |
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) |
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txt.submit(predict, [txt, state], [chatbot, state]) |
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gr.HTML( |
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"️<center> Created with ❤️ by @louis_ulmer & @aurelien_lac" |
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) |
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app.launch(auth=("cellule_ia", "pass")) |
