app1.py

import os
import torch
import gradio as gr
from PIL import Image
from huggingface_hub import login
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from quanto import quantize_model

# === [AUTHENTICATION] ===
hf_token = os.getenv("hf_token")
if hf_token is None:
    raise ValueError("Please set HF_TOKEN environment variable with your Hugging Face token")
login(token=hf_token)

# === [TRANSLATOR] ===
translator = pipeline("translation", model="facebook/nllb-200-distilled-600M")

# === [LOAD & QUANTIZE MODEL] ===
model_name = "ContactDoctor/Bio-Medical-Llama-3-2-1B-CoT-012025"
tokenizer = AutoTokenizer.from_pretrained(model_name)

print("Loading base model...")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

print("Quantizing model...")
quantized_model = quantize_model(model, bits=4)

print("Initializing pipeline...")
text_gen_pipeline = pipeline(
    "text-generation",
    model=quantized_model,
    tokenizer=tokenizer,
    torch_dtype=torch.float16,
    device_map="auto"
)

# === [SYSTEM MESSAGE] ===
system_message = {
    "role": "system",
    "content": (
        "You are a helpful, respectful, and knowledgeable medical assistant developed by the AI team at AfriAI Solutions, Senegal. "
        "Provide brief, clear definitions when answering medical questions. After giving a concise response, ask the user if they would like more information about symptoms, causes, or treatments. "
        "Always encourage users to consult healthcare professionals for personalized advice."
    )
}

messages = [system_message]
max_history = 10

salutations = ["bonjour", "salut", "bonsoir", "coucou"]
remerciements = ["merci", "je vous remercie", "thanks"]
au_revoir = ["au revoir", "à bientôt", "bye", "bonne journée", "à la prochaine"]

def detect_smalltalk(user_input):
    lower_input = user_input.lower().strip()
    if any(phrase in lower_input for phrase in salutations):
        return "Bonjour ! Comment puis-je vous aider aujourd'hui ?", True
    if any(phrase in lower_input for phrase in remerciements):
        return "Avec plaisir ! Souhaitez-vous poser une autre question médicale ?", True
    if any(phrase in lower_input for phrase in au_revoir):
        return "Au revoir ! Prenez soin de votre santé et n'hésitez pas à revenir si besoin.", True
    return "", False

def medical_chatbot(user_input):
    global messages

    smalltalk_response, handled = detect_smalltalk(user_input)
    if handled:
        return smalltalk_response

    translated = translator(user_input, src_lang="fra_Latn", tgt_lang="eng_Latn")[0]['translation_text']
    
    messages.append({"role": "user", "content": translated})
    if len(messages) > max_history * 2:
        messages = [system_message] + messages[-max_history * 2:]

    prompt = text_gen_pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)

    response = text_gen_pipeline(
        prompt,
        max_new_tokens=1024,
        do_sample=True,
        temperature=0.4,
        top_k=150,
        top_p=0.75,
        eos_token_id=[
            text_gen_pipeline.tokenizer.eos_token_id,
            text_gen_pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
        ]
    )

    output = response[0]['generated_text'][len(prompt):].strip()
    translated_back = translator(output, src_lang="eng_Latn", tgt_lang="fra_Latn")[0]['translation_text']
    
    messages.append({"role": "assistant", "content": translated_back})
    return translated_back

# === [LOGO LOAD] ===
logo = Image.open("AfriAI Solutions.jpg")

# === [GRADIO UI] ===
with gr.Blocks(theme=gr.themes.Soft(primary_hue="indigo")) as demo:
    with gr.Row():
        gr.Image(value=logo, show_label=False, show_download_button=False, interactive=False, height=150)

    gr.Markdown("""
    # 🤖 Chatbot Médical AfriAI Solutions
    **Posez votre question médicale en français.**  
    Le chatbot vous répondra brièvement et avec bienveillance, puis vous demandera si vous souhaitez plus de détails.
    """, elem_id="title")

    chatbot = gr.Chatbot(label="Chat avec le Médecin Virtuel")
    msg = gr.Textbox(label="Votre question", placeholder="Exemple : Quels sont les symptômes du paludisme ?")
    clear = gr.Button("Effacer la conversation", variant="secondary")

    def respond(message, history):
        response = medical_chatbot(message)
        history = history or []
        history.append((message, response))
        return "", history

    msg.submit(respond, [msg, chatbot], [msg, chatbot])
    clear.click(lambda: ("", []), None, [msg, chatbot])

demo.launch()