Initial chatbot

app.py

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+import gradio as gr
+
+from transformers import (
+    AutoTokenizer, 
+    AutoModelForSeq2SeqLM, 
+    AutoModelForCausalLM, 
+    BitsAndBytesConfig,
+    pipeline
+)
+
+import torch
+
+# CHAT MODEL
+
+chat_modelcard = 'meta-llama/Llama-3.2-3B-Instruct'
+
+tokenizer = AutoTokenizer.from_pretrained(chat_modelcard)
+model = AutoModelForCausalLM.from_pretrained(chat_modelcard, quantization_config=BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16), device_map='cpu')
+
+chat_pipeline = pipeline('text-generation', model=model, tokenizer=tokenizer, do_sample=True, temperature=0.5, truncation=True, max_length=512, return_full_text=False)
+
+# TRANSLATION MODELS
+
+fw_modelcard = "amurienne/gallek-m2m100"
+bw_modelcard = "amurienne/kellag-m2m100"
+
+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
+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']
+
+# answer function
+def answer(text):
+     return chat_pipeline(text, chat_template=None)[0]['generated_text']
+
+def format_prompt_with_history(message, native_chat_history):
+    # format the conversation history
+    prompt = ""
+    for interaction in native_chat_history:
+        prompt += f"<|start_header_id|>{interaction['role']}<|end_header_id|>\n{interaction['content']}<|eot_id|>\n"
+
+    # add the current user message
+    prompt += f"<|start_header_id|>user<|end_header_id|>\ntu es un assistant francophone. Répond en une seule phrase sans formattage.<|eot_id|>\n<|start_header_id|>assistant<|end_header_id|>\n"
+
+    return prompt
+
+# maximum number of interactions to keep in history
+max_history_length = 3
+
+# keep a hidden model "native" language chat history
+native_chat_history = []
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    chatbot = gr.Chatbot(label="Breton Chatbot (Translation based)", 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 respond(message, chat_history):
+        """
+        Handles bot response generation
+        """
+
+        global native_chat_history
+
+        fr_message = translate(message, forward=False)
+        print(f"user fr -> {fr_message}")
+
+        prompt = format_prompt_with_history(fr_message, native_chat_history)
+
+        bot_fr_message = answer(prompt)
+        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": "user", "content": 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
+
+    msg.submit(respond, [msg, chatbot], [msg, chatbot])
+
+if __name__ == "__main__":
+    demo.launch()