import transformers import numpy as np import re from transformers import AutoConfig, AutoTokenizer, AutoModel, AutoModelForCausalLM from vllm import LLM, SamplingParams import torch import gradio as gr import json import os import shutil import requests from pprint import pprint import chromadb import pandas as pd from sklearn.metrics.pairwise import cosine_similarity pd.set_option('display.max_columns', None) sampling_params = SamplingParams(temperature=.7, top_p=.95, max_tokens=2000, presence_penalty = 1.5, stop = ["``"]) # Define the device device = "cuda" if torch.cuda.is_available() else "cpu" #Define variables temperature=0.2 max_new_tokens=1000 top_p=0.92 repetition_penalty=1.7 model_name = "Inagua/code-model" llm = LLM(model_name, max_model_len=4096) #CSS for references formatting css = """ .generation { margin-left:2em; margin-right:2em; } :target { background-color: #CCF3DF; /* Change the text color to red */ } .source { float:left; max-width:17%; margin-left:2%; } .tooltip { position: relative; cursor: pointer; font-variant-position: super; color: #97999b; } .tooltip:hover::after { content: attr(data-text); position: absolute; left: 0; top: 120%; /* Adjust this value as needed to control the vertical spacing between the text and the tooltip */ white-space: pre-wrap; /* Allows the text to wrap */ width: 500px; /* Sets a fixed maximum width for the tooltip */ max-width: 500px; /* Ensures the tooltip does not exceed the maximum width */ z-index: 1; background-color: #f9f9f9; color: #000; border: 1px solid #ddd; border-radius: 5px; padding: 5px; display: block; box-shadow: 0 4px 8px rgba(0,0,0,0.1); /* Optional: Adds a subtle shadow for better visibility */ }""" #Curtesy of chatgpt def format_references(text): # Define start and end markers for the reference ref_start_marker = '', start_pos) if end_pos == -1: # Malformed reference, break to avoid infinite loop break # Extract the reference text ref_text = text[start_pos + len(ref_start_marker):end_pos].replace('\n', ' ').strip() ref_text_encoded = ref_text.replace("&", "&").replace("<", "<").replace(">", ">") # Find the end of the reference tag ref_end_pos = text.find(ref_end_marker, end_pos) if ref_end_pos == -1: # Malformed reference, break to avoid infinite loop break # Extract the reference ID ref_id = text[end_pos + 2:ref_end_pos].strip() # Create the HTML for the tooltip tooltip_html = f'[' + str(ref_number) +']' parts.append(tooltip_html) # Update current_pos to the end of the current reference current_pos = ref_end_pos + len(ref_end_marker) ref_number = ref_number + 1 # Join and return the parts parts = ''.join(parts) return parts # Class to encapsulate the Falcon chatbot class MistralChatBot: def __init__(self, system_prompt="Le dialogue suivant est une conversation"): self.system_prompt = system_prompt def predict(self, user_message, context): detailed_prompt = """### Question ###\n""" + user_message + "\n\n### Contexte ###\n" + context + "\n\n### Formule ###\n" prompts = [detailed_prompt] outputs = llm.generate(prompts, sampling_params, use_tqdm = False) generated_text = outputs[0].outputs[0].text fiches_html = "" return generated_text, fiches_html # Create the Falcon chatbot instance mistral_bot = MistralChatBot() # Define the Gradio interface title = "Inagua" description = "An experimental LLM to interact with DAMAaaS documentation" examples = [ [ "How to calculate a linear regression?", # user_message 0.7 # temperature ] ] additional_inputs=[ gr.Slider( label="Température", value=0.2, # Default value minimum=0.05, maximum=1.0, step=0.05, interactive=True, info="Des valeurs plus élevées donne plus de créativité, mais aussi d'étrangeté", ), ] demo = gr.Blocks() with gr.Blocks(theme='gradio/monochrome', css=css) as demo: gr.HTML("""

InaguaLLM

""") text_input = gr.Textbox(label="Your question", type="text", lines=1) context_input = gr.Textbox(label="Your context", type="text", lines=1) text_button = gr.Button("Query InaguaLLM") text_output = gr.HTML(label="Answer") text_button.click(mistral_bot.predict, inputs=[text_input, context_input], outputs=[text_output], api_name="convert-code") if __name__ == "__main__": demo.queue().launch()