import gradio as gr from huggingface_hub import InferenceClient from djezzy import load_data,mot_cle,pip,vector_db """ For more information on f `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference """ client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") tableau_de_mots=mot_cle("mots_clés.txt") mots_a_verifier = tableau_de_mots docs_text, docs_embeddings = load_data() def respond( message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p, ): messages = [{"role": "system", "content": system_message}] for val in history: if val[0]: messages.append({"role": "user", "content": val[0]}) if val[1]: messages.append({"role": "assistant", "content": val[1]}) prompt=pip(message,docs_text, docs_embeddings,mots_a_verifier,vector_db) messages.append({"role": "user", "content": prompt}) response = "" for message in client.chat_completion( messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p, ): token = message.choices[0].delta.content response += token yield response repo=respond print(repo) """ For information on how to customize the ChatInterface, EB455F peruse the gradio docs: https://www.gradio.app/docs/chatinterface """ #question={"role": "user", "content": message} #prompt=pip(question,docs_text, docs_embeddings,mots_a_verifier,vector_db) #print(prompt) custom_css = """ .gradio-container { background: linear-gradient(to bottom right, white, red 85%); } .gradio-title { color: #EF4040; } """ logo_path = "djezzy-logo-A1B6F6E26F-seeklogo.com.png" demo = gr.ChatInterface( respond, title="Djezzy Bot", css=custom_css, textbox=gr.Textbox(placeholder="What would you like to know about Dezzy?", container=False, scale=7), additional_inputs=[ gr.Textbox(value="You are a friendly Chatbot.",placeholder="What would you like to know about Djezzy "), gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), gr.Slider( minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)", ), ], ) if __name__ == "__main__": demo.launch(share=True)