app.py

import gradio as gr
from huggingface_hub import login
from smolagents import HfApiModel, Tool, CodeAgent

import os
import sys
import json

if './lib' not in sys.path :
    sys.path.append('./lib')
from ingestion_chroma import retrieve_info_from_db

############################################################################################
################################### TOOLS ##################################################
############################################################################################

def search_key(d, target_key):
    """
    Recherche une clé dans un dictionnaire imbriqué.

    :param d: Le dictionnaire dans lequel chercher.
    :param target_key: La clé à chercher.
    :return: Une liste des valeurs associées à la clé trouvée.
    """
    results = []

    def recursive_search(d):
        if isinstance(d, dict):
            for key, value in d.items():
                if key == target_key:
                    results.append(value)
                if isinstance(value, dict):
                    recursive_search(value)
                elif isinstance(value, list):
                    for item in value:
                        if isinstance(item, dict):
                            recursive_search(item)

    recursive_search(d)
    if len(results)>0:
        return str(results[0])
    else :
        return "Indicator not found. Try globals indicators in this list : ['ESRS E4', 'ESRS 2 MDR', 'ESRS S2', 'ESRS E2', 'ESRS S4', 'ESRS E5', 'ESRS 2', 'ESRS E1', 'ESRS S3', 'ESRS S1', 'ESRS G1', 'ESRS E3']"
    
    

############################################################################################

class Chroma_retrieverTool(Tool):
    name = "request"
    description = "Using semantic similarity, retrieve the text from the knowledge base that has the embedding closest to the query."
    inputs = {
        "query": {
            "type": "string",
            "description": "The query to execute must be semantically close to the text to search. Use the affirmative form rather than a question.",
        },
    }
    output_type = "string"
    
    def forward(self, query: str) -> str:
        assert isinstance(query, str), "The request needs to be a string."
        
        query_results = retrieve_info_from_db(query)
        str_result = "\nRetrieval texts : \n" + "".join([f"===== Text {str(i)} =====\n" + query_results['documents'][0][i] for i in range(len(query_results['documents'][0]))])

        return str_result
        
############################################################################################

class ESRS_info_tool(Tool):
    name = "find_ESRS"
    description = "Find ESRS description to help you to find what indicators the user want to analyze"
    inputs = {
        "indicator": {
            "type": "string",
            "description": "The indicator name with format for example like following 'ESRS EX' or 'EX'. return the description of the indicator demanded.",
        },
    }
    output_type = "string"
    
    def forward(self, indicator: str) -> str:
        assert isinstance(indicator, str), "The request needs to be a string."

        with open('./data/dico_esrs.json') as json_data:
            dico_esrs = json.load(json_data)
        
        result = search_key(dico_esrs, indicator)

        return result
        
############################################################################################
############################################################################################
############################################################################################

def respond(message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,):
    system_prompt_added = """You are an expert in environmental and corporate social responsibility. You must respond to requests using the query function in the document database. 
User's question : """
    agent_output = agent.run(system_prompt_added + message)
    
    yield agent_output
    
############################################################################################
hf_token = os.getenv("HF_TOKEN_all")
login(hf_token)
model = HfApiModel("Qwen/Qwen2.5-Coder-32B-Instruct")

retriever_tool = Chroma_retrieverTool()
get_ESRS_info_tool = ESRS_info_tool()
agent = CodeAgent(
    tools=[
            get_ESRS_info_tool,
            retriever_tool,
          ],
    model=model,
    max_steps=10,
    max_print_outputs_length=16000,
    additional_authorized_imports=['pandas', 'matplotlib', 'datetime']
    )


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        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()