app.py

import transformers
import re
from transformers import AutoConfig, AutoTokenizer, AutoModel, AutoModelForCausalLM
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"

model_name = "PleIAs/Pleias-Rag"

# Get Hugging Face token from environment variable
hf_token = os.environ.get('HF_TOKEN')
if not hf_token:
    raise ValueError("Please set the HF_TOKEN environment variable")

# Initialize model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token)
model = AutoModelForCausalLM.from_pretrained(model_name, token=hf_token)
model.to(device)

# Set tokenizer configuration
tokenizer.eos_token = "<|answer_end|>"
eos_token_id=tokenizer.eos_token_id
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = 1

# Define variables 
temperature = 0.0  
max_new_tokens = 1400
top_p = 0.95
repetition_penalty = 1.0
min_new_tokens = 700
early_stopping = False

# Connect to the LanceDB database
db = lancedb.connect("content19/lancedb_data")
table = db.open_table("edunat19")

def hybrid_search(text):
    results = table.search(text, query_type="hybrid").limit(5).to_pandas()

    document = []
    document_html = []
    for _, row in results.iterrows():
        hash_id = str(row['hash'])
        title = row['section']
        content = row['text']

        document.append(f"<|source_start|><|source_id_start|>{hash_id}<|source_id_end|>{title}\n{content}<|source_end|>")
        
        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 pleiasBot:
    def __init__(self, system_prompt="Tu es Appli, un asistant de recherche qui donne des responses sourcées"):
        self.system_prompt = system_prompt

    def predict(self, user_message):
        fiches, fiches_html = hybrid_search(user_message)
        
        detailed_prompt = f"""<|query_start|>{user_message}<|query_end|>\n{fiches}\n<|source_analysis_start|>"""

        # Convert inputs to tensor
        input_ids = tokenizer.encode(detailed_prompt, return_tensors="pt").to(device)
        attention_mask = torch.ones_like(input_ids)

        try:
            output = model.generate(
                input_ids,
                attention_mask=attention_mask,
                max_new_tokens=max_new_tokens,
                do_sample=False,
                early_stopping=early_stopping,
                min_new_tokens=min_new_tokens,
                temperature=temperature,
                repetition_penalty=repetition_penalty,
                pad_token_id=tokenizer.pad_token_id,
                eos_token_id=tokenizer.eos_token_id
            )

            # Decode the generated text
            generated_text = tokenizer.decode(output[0][len(input_ids[0]):])
            
            # Split the text into analysis and answer sections
            parts = generated_text.split("<|source_analysis_end|>")
            if len(parts) == 2:
                analysis = parts[0].strip()
                answer = parts[1].replace("<|answer_start|>", "").replace("<|answer_end|>", "").strip()
                
                # Format each section with matching h2 titles
                analysis_text = '<h2 style="text-align:center">Analyse des sources</h2>\n<div class="generation">' + format_references(analysis) + "</div>"
                answer_text = '<h2 style="text-align:center">Réponse</h2>\n<div class="generation">' + format_references(answer) + "</div>"
            else:
                analysis_text = ""
                answer_text = format_references(generated_text)
            
            fiches_html = '<h2 style="text-align:center">Sources</h2>\n' + fiches_html
            return analysis_text, answer_text, fiches_html

        except Exception as e:
            print(f"Error during generation: {str(e)}")
            import traceback
            traceback.print_exc()
            return None, None, None

def format_references(text):
    ref_pattern = r'<ref name="([^"]+)">"([^"]+)"</ref>'
    
    parts = []
    current_pos = 0
    ref_number = 1

    import re
    for match in re.finditer(ref_pattern, text):
        # Add text before the reference
        parts.append(text[current_pos:match.start()])
        
        # Extract reference components
        ref_id = match.group(1)  # The source ID
        ref_text = match.group(2).strip()  # The reference text
        
        # Create tooltip HTML with source ID in bold
        tooltip_html = f'<span class="tooltip">[{ref_number}]<span class="tooltiptext"><strong>{ref_id}</strong>: {ref_text}</span></span>'
        parts.append(tooltip_html)
        
        current_pos = match.end()
        ref_number += 1
    
    # Add any remaining text
    parts.append(text[current_pos:])
    
    return ''.join(parts)

# Initialize the pleiasBot
pleias_bot = pleiasBot()

# 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;
    display: inline-block;
    color: #2563eb;
    cursor: pointer;
}
.tooltip .tooltiptext {
    visibility: hidden;
    background-color: #fff;
    color: #000;
    text-align: left;
    padding: 12px;
    border-radius: 6px;
    border: 1px solid #e5e7eb;
    box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06);
    position: absolute;
    z-index: 1;
    bottom: 125%;
    left: 50%;
    transform: translateX(-50%);
    min-width: 300px;
    max-width: 400px;
    white-space: normal;
    font-size: 0.9em;
    line-height: 1.4;
}
.tooltip:hover .tooltiptext {
    visibility: visible;
}
.tooltip .tooltiptext::after {
    content: "";
    position: absolute;
    top: 100%;
    left: 50%;
    margin-left: -5px;
    border-width: 5px;
    border-style: solid;
    border-color: #fff transparent transparent transparent;
}
.section-title {
    font-weight: bold;
    font-size: 15px;
    margin-bottom: 1em;
    margin-top: 1em;
}
"""

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

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

with demo:
    # Header with black bar
    gr.HTML("""
    <div style="display: flex; justify-content: center; width: 100%; background-color: black; padding: 10px 0;">
        <pre style="font-family: monospace; line-height: 1.2; font-size: 24px; color: #00ffea; margin: 0;">
╔═══════════════════╗
║   pleias-RAG 1.0  ║
╚═══════════════════╝
        </pre>
    </div>
    """)
    
    with gr.Row():
        # Left column for input, button and answer
        with gr.Column(scale=2):
            text_input = gr.Textbox(label="Votre question ou votre instruction", lines=3)
            text_button = gr.Button("Interroger pleias-RAG")
            response_output = gr.HTML(label="Réponse")
            
        # Right column for analysis
        with gr.Column(scale=3):
            text_output = gr.HTML(label="Analyse des sources")
            
    # Sources at the bottom
    with gr.Row():
        embedding_output = gr.HTML(label="Les sources utilisées")
    
    text_button.click(gradio_interface, 
                     inputs=text_input, 
                     outputs=[text_output, response_output, embedding_output])

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