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| import gradio as gr | |
| import numpy as np | |
| import random | |
| import time | |
| import os | |
| import shutil | |
| import codecs | |
| # How to RUN code ==> gradio gradio_llm_example.py | |
| # Define text and title information | |
| title1 = "## </br> </br> </br> 🤗💬 QA App" | |
| title2 = " ## </br> </br> </br> Gradio QA Bot" | |
| intro = """ Welcome! This is not just any bot, it's a special one equipped with state-of-the-art natural language processing capabilities, and ready to answer your queries. | |
| Ready to explore? Let's get started! | |
| * Step 1: Upload a PDF document. | |
| * Step 2: Type in a question related to your document's content. | |
| * Step 3: Get your answer! | |
| Push clear cache before uploading a new doc! | |
| """ | |
| about = """ | |
| ## </br> About | |
| This app is an LLM-powered chatbot built using: | |
| - [Streamlit](<https://streamlit.io/>) | |
| - [HugChat](<https://github.com/Soulter/hugging-chat-api>) | |
| - Chat Model = llama2-chat-hf 7B | |
| - Retreiver model = all-MiniLM-L6-v2 | |
| </br> | |
| 💡 Note: No API key required! | |
| </br> | |
| Made with ❤️ by us | |
| """ | |
| # Define theme ==> see gr.themes.builder() | |
| theme = gr.themes.Soft( | |
| primary_hue="emerald", | |
| secondary_hue="emerald", | |
| neutral_hue="slate", | |
| ).set( | |
| body_background_fill_dark='*primary_50', | |
| shadow_drop='*shadow_spread', | |
| button_border_width='*block_border_width', | |
| button_border_width_dark='*block_label_border_width' | |
| ) | |
| def upload_file(files_obj): | |
| """ Upload several files from drag and drop, and save them in local temp folder | |
| files_obj (type:list) : list of tempfile._TemporaryFileWrapper | |
| return checkbox to display uploaded documents """ | |
| # Create local copy | |
| temp_file_path = "./temp" | |
| if not os.path.exists(temp_file_path): | |
| os.makedirs(temp_file_path) | |
| # Save each file among list of given files | |
| file_name_list = list() | |
| for file_obj in files_obj : | |
| file_name = os.path.basename(file_obj.name) | |
| file_name_list.append(file_name) | |
| shutil.copyfile(file_obj.name, os.path.join(temp_file_path, file_name)) | |
| return {uploaded_check : gr.Radio(choices=file_name_list, visible=True), | |
| choose_btn : gr.Button(value="Choose", visible=True)} | |
| def read_content(file_name): | |
| print(file_name, type(file_name)) | |
| temp_file_path = "./temp" | |
| file_path = os.path.join(temp_file_path, file_name) | |
| with open(file_path, "rb") as file: | |
| try: | |
| content = file.read() | |
| print(content) | |
| print(codecs.decode(content, 'utf-8')) | |
| return {error_box: gr.Textbox(value=f"File ready to be used. \n You can ask a question about the uploaded PDF document.", visible=True)} | |
| except Exception as e: | |
| print(f"Error occurred while writing the file: {e}") | |
| return {error_box: gr.Textbox(value=f"Error occurred while writing the file: {e}", visible=True)} | |
| def respond(message, chat_history, | |
| language_choice, max_length, temperature, | |
| num_return_sequences, top_p, no_repeat_ngram_size): | |
| #No LLM here, just respond with a random pre-made message | |
| if content == "": | |
| bot_message = f"j'ai {max_length}" + random.choice(["Tell me more about it", | |
| "Cool, but I'm not interested", | |
| "Hmmmm, ok then"]) | |
| else: | |
| bot_message = " j'ai besoin d'un modèle pour lire le {content[:3]}" | |
| chat_history.append((message, bot_message)) | |
| return "", chat_history | |
| # Layout | |
| with gr.Blocks(theme=gr.themes.Soft()) as gradioApp: | |
| with gr.Row(): | |
| with gr.Column(scale=1, min_width=100): | |
| logo_gr = gr.Markdown(""" <img src="file/logo_neovision.png" alt="logo" style="width:400px;"/>""") | |
| # gr.Image("./logo_neovision.png") | |
| about_gr = gr.Markdown(about) | |
| with gr.Column(scale=2, min_width=500): | |
| title1_gr= gr.Markdown(title1) | |
| intro_gr = gr.Markdown(intro) | |
| # Upload several documents | |
| content = "" | |
| upload_button = gr.UploadButton("Browse files", label="Drag and drop your documents here", | |
| size="lg", scale=0, min_width=100, | |
| file_types=["pdf"], file_count="multiple") | |
| uploaded_check = gr.Radio(label="Uploaded documents", visible=False, | |
| info="Do you want to use a supporting document?") | |
| choose_btn = gr.Button(value="Choose", visible=False) | |
| upload_button.upload(upload_file, upload_button, [uploaded_check, choose_btn]) | |
| # Read only one document | |
| error_box = gr.Textbox(label="Reading files... ", visible=False) | |
| choose_btn.click(read_content, inputs=uploaded_check, outputs=error_box) | |
| # Select advanced options | |
| gr.Markdown(""" ## Toolbox """) | |
| with gr.Accordion(label="Select advanced options",open=False): | |
| language_choice = gr.Dropdown(["English", "French"], label="Language", info="Choose your language") | |
| max_length = gr.Slider(label="Token length", minimum=1, maximum=100, value=50, step=1) | |
| temperature= gr.Slider(label="Temperature", minimum=0.1, maximum=1, value=0.8, step=0.1) | |
| num_return_sequences= gr.Slider(label="Temperature", minimum=0.1, maximum=50, value=1, step=0.1) | |
| top_p= gr.Slider(label="Temperature", minimum=0.1, maximum=1, value=0.8, step=0.1) | |
| no_repeat_ngram_size= gr.Slider(label="Temperature", minimum=0.1, maximum=1, value=3, step=0.1) | |
| # Chat | |
| with gr.Column(scale=2, min_width=600): | |
| title2_gr = gr.Markdown(title2) | |
| chatbot = gr.Chatbot(label="Bot", height=500) | |
| msg = gr.Textbox(label="User", placeholder="Ask any question.") | |
| chatbot_btn = gr.Button("Submit") | |
| chatbot_btn.click(respond, inputs=[msg, chatbot, | |
| language_choice, max_length, temperature, | |
| num_return_sequences, top_p, no_repeat_ngram_size], | |
| outputs=[msg, chatbot]) | |
| clear = gr.ClearButton(components=[msg, chatbot], value="Clear console") | |
| gr.close_all() | |
| gradioApp.launch(share=True, enable_queue=True) | |