app.py

# The MIT License

# Copyright (c) 2025 Albert Murienne

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.

import os
import spaces
import torch

import gradio as gr

from transformers import (
    AutoTokenizer, 
    AutoModelForSeq2SeqLM, 
    pipeline
)

from huggingface_hub import InferenceClient

device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"current device is: {device}")

# CHAT MODEL

class chat_engine_hf_api:

    def __init__(self):
        self.client = InferenceClient(
            "microsoft/Phi-3.5-mini-instruct",
            #"meta-llama/Llama-3.2-3B-Instruct",
            token=os.environ['HF_TOKEN_API']
        )

    def answer(self, message, history):
        return self.client.chat_completion(
            history + [{"role": "user", "content": f"tu es un assistant francophone. Répond en une seule phrase sans formattage.\n{message}"}], 
            max_tokens=512, 
            temperature = 0.5).choices[0].message.content

chat_engine = chat_engine_hf_api()

# TRANSLATION MODELS

fw_modelcard = "amurienne/gallek-m2m100-v0.2"
bw_modelcard = "amurienne/kellag-m2m100-v0.2"

fw_model = AutoModelForSeq2SeqLM.from_pretrained(fw_modelcard)
fw_tokenizer = AutoTokenizer.from_pretrained(fw_modelcard)

fw_translation_pipeline = pipeline("translation", model=fw_model, tokenizer=fw_tokenizer, src_lang='fr', tgt_lang='br', max_length=400, device="cpu")

bw_model = AutoModelForSeq2SeqLM.from_pretrained(bw_modelcard)
bw_tokenizer = AutoTokenizer.from_pretrained(bw_modelcard)

bw_translation_pipeline = pipeline("translation", model=bw_model, tokenizer=bw_tokenizer, src_lang='br', tgt_lang='fr', max_length=400, device="cpu")

# translation function
@spaces.GPU
def translate(text, forward: bool):
    if forward:
        return fw_translation_pipeline("traduis de français en breton: " + text)[0]['translation_text']
    else:
        return bw_translation_pipeline("treiñ eus ar galleg d'ar brezhoneg: " + text)[0]['translation_text']

# maximum number of interactions to keep in history
max_history_length = 3

# keep a hidden model "native" language chat history
native_chat_history = []

# example queries
example_queries = [{"text" : "Petra eo ar rekipe krampouezh ?"}, {"text": "Pelec'h emañ Pariz ?"}, {"text" : "Petra eo kêr vrasañ Breizh ?"}, {"text" : "Kont din ur farsadenn bugel ?"}]

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    
    gr.Markdown("# BreizhBot\n## Breton Chatbot (Translation based)\nPart of the [GweLLM](https://github.com/blackccpie/GweLLM) project")
    
    chatbot = gr.Chatbot(
        label="Chat",
        placeholder="Degemer mat, petra a c'hellan ober evidoc'h ?",
        examples=example_queries,
        type="messages")
    msg = gr.Textbox(label='User Input')

    def clear(chat_history):
        """
        Handles clearing chat
        """
        chat_history.clear()
        native_chat_history.clear()

    chatbot.clear(clear, inputs=[chatbot])

    def example_input(evt: gr.SelectData):
        """
        Handles example input selection
        """
        return evt.value["text"]
    
    def user_input(message, chat_history):
        """
        Handles instant display of the user query (without waiting for model answer)
        """
        chat_history.append({"role": "user", "content": message})
        return chat_history

    def respond(message, chat_history):
        """
        Handles bot response generation
        """

        global native_chat_history

        fr_message = translate(message, forward=False)
        print(f"user fr -> {fr_message}")

        bot_fr_message = chat_engine.answer(fr_message, native_chat_history)
        print(f"bot fr -> {bot_fr_message}")
        bot_br_message = translate( bot_fr_message, forward=True)
        print(f"bot br -> {bot_br_message}")

        chat_history.append({"role": "assistant", "content": bot_br_message})

        native_chat_history.append({"role": "user", "content": fr_message})
        native_chat_history.append({"role": "assistant", "content": bot_fr_message})

        # limit the history length
        if len(chat_history) > max_history_length * 2:
            chat_history = chat_history[-max_history_length * 2:]
            native_chat_history = native_chat_history[-max_history_length * 2:]

        return "", chat_history

    chatbot.example_select(example_input, None, msg).then(user_input, [msg, chatbot], chatbot).then(respond, [msg, chatbot], [msg, chatbot])
        
    msg.submit(user_input, [msg, chatbot], chatbot).then(respond, [msg, chatbot], [msg, chatbot])

if __name__ == "__main__":
    demo.launch()