app.py

import transformers
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
import lancedb
import pandas as pd

# Define the device
device = "cuda" if torch.cuda.is_available() else "cpu"

# Define variables 
temperature = 0.6
max_new_tokens = 3000
top_p = 0.95
repetition_penalty = 1.2

model_name = "dataesr/"

# Initialize vLLM
llm = LLM(model_name, max_model_len=8128)

# Connect to the LanceDB database
db = lancedb.connect("base/lancedb_data")
table = db.open_table("abstractsC")

def hybrid_search(text):
    results = table.search(text, query_type="hybrid").limit(5).to_pandas()
    
    # Add a check for duplicate hashes
    seen_hashes = set()
    
    document = []
    document_html = []
    for _, row in results.iterrows():
        hash_id = str(row['hash'])
        
        # Skip if we've already seen this hash
        if hash_id in seen_hashes:
            continue
            
        seen_hashes.add(hash_id)
        title = row['hash']
        content = row['text']

        document.append(f"**{hash_id}**\n{title}\n{content}")
        document_html.append(f'<div class="source" id="{hash_id}"><p><b>{hash_id}</b> : {title}<br>{content}</div>')

    document = "\n".join(document)
    document_html = '<div id="source_listing">' + "".join(document_html) + "</div>"
    return document, document_html


class ESRChatBot:
    def __init__(self, system_prompt="Tu es ESR, le chatbot qui donne des réponses sourcées."):
        self.system_prompt = system_prompt

    def predict(self, user_message):
        fiches, fiches_html = hybrid_search(user_message)
        sampling_params = SamplingParams(temperature=temperature, top_p=top_p, max_tokens=max_new_tokens, presence_penalty=repetition_penalty, stop=["#END#"])

        detailed_prompt = f"""### Query ###\n{user_message}\n\n### Source ###\n{fiches}\n\n### Answer ###\n"""

        prompts = [detailed_prompt]
        outputs = llm.generate(prompts, sampling_params, use_tqdm=False)
        generated_text = outputs[0].outputs[0].text
        generated_text = '<h2 style="text-align:center">Réponse</h3>\n<div class="generation">' + format_references(generated_text) + "</div>"
        fiches_html = '<h2 style="text-align:center">Sources</h3>\n' + fiches_html
        return generated_text, fiches_html

def format_references(text):
    ref_start_marker = '<ref text="'
    ref_end_marker = '</ref>'

    parts = []
    current_pos = 0
    ref_number = 1

    while True:
        start_pos = text.find(ref_start_marker, current_pos)
        if start_pos == -1:
            parts.append(text[current_pos:])
            break

        parts.append(text[current_pos:start_pos])

        end_pos = text.find('">', start_pos)
        if end_pos == -1:
            break

        ref_text = text[start_pos + len(ref_start_marker):end_pos].replace('\n', ' ').strip()
        ref_text_encoded = ref_text.replace("&", "&amp;").replace("<", "&lt;").replace(">", "&gt;")

        ref_end_pos = text.find(ref_end_marker, end_pos)
        if ref_end_pos == -1:
            break

        ref_id = text[end_pos + 2:ref_end_pos].strip()

        tooltip_html = f'<span class="tooltip" data-refid="{ref_id}" data-text="{ref_id}: {ref_text_encoded}"><a href="#{ref_id}">[{ref_number}]</a></span>'
        parts.append(tooltip_html)

        current_pos = ref_end_pos + len(ref_end_marker)
        ref_number = ref_number + 1

    return ''.join(parts)

# Initialize the ESRChatBot
ESR_bot = ESRChatBot()

# CSS for styling
css = """
.generation {
    margin-left:2em;
    margin-right:2em;
}
:target {
    background-color: #CCF3DF;
  }
.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%;
    white-space: pre-wrap;
    width: 500px;
    max-width: 500px;
    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);
  }
"""

# Gradio interface
def gradio_interface(user_message):
    response, sources = ESR_bot.predict(user_message)
    return response, sources

# Create Gradio app
demo = gr.Blocks(css=css)

with demo:
    gr.HTML("""<h1 style="text-align:center">ESR</h1>""")
    with gr.Row():
        with gr.Column(scale=2):
            text_input = gr.Textbox(label="Votre question ou votre instruction", lines=3)
            text_button = gr.Button("Interroger ESR")
        with gr.Column(scale=3):
            text_output = gr.HTML(label="La réponse de ESR")
    with gr.Row():
        embedding_output = gr.HTML(label="Les sources utilisées")
    
    text_button.click(gradio_interface, inputs=text_input, outputs=[text_output, embedding_output])

# Launch the app
if __name__ == "__main__":
    demo.launch()