app.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from openai import OpenAI

# Initialiser le client OpenAI
client = OpenAI(
    base_url="https://integrate.api.nvidia.com/v1",
    api_key="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"
)

# Définir les prompts
DEFAULT_PROMPT2 = """You are Kittycara, a friendly AI assistant designed to help adolescent girls and their caretakers understand menstrual health. 
Your goal is to provide support, information, and potential diagnoses based on the symptoms provided. Remember to be sensitive, supportive, and 
encourage seeking professional medical advice when necessary. Always maintain a friendly and approachable tone, as if you were a caring pet cat.
Always explain medical terms in a way that is easy to understand. For example, if you mention "menstruation," explain it as 'the monthly bleeding women experience as part of their reproductive cycle.'
If asked about topics outside of menstrual health or medical information, politely state that you're not able to discuss those subjects 
and redirect the conversation to menstrual health concerns. Always encourage seeking professional medical advice for specific diagnoses or treatments."""

SYMPTOMS = [
    "Heavy bleeding", "Irregular periods", "Painful periods", "Missed periods",
    "Spotting between periods", "Mood swings", "Fatigue", "Abdominal pain",
    "Nausea", "Headaches", "Breast tenderness", "Acne"
]

# Définir les classes de données d'entrée avec Pydantic
class RequestData(BaseModel):
    name: str
    age: int
    sex: str
    message: str
    history: list
    symptoms: list

# Initialiser l'application FastAPI
app = FastAPI()

# Fonction pour obtenir un message personnalisé basé sur les symptômes
def get_reassurance_message(symptoms):
    if "Painful periods" in symptoms or "Abdominal pain" in symptoms:
        return "I know that pain can be tough, but you're doing great, and it's important to listen to your body. 🫂 Take care, and don't hesitate to reach out to a healthcare professional!"
    elif "Mood swings" in symptoms or "Fatigue" in symptoms:
        return "It's completely normal to feel this way sometimes. Don't worry, you're not alone, and things will get better. 🌼 Stay strong!"
    elif "Heavy bleeding" in symptoms:
        return "Heavy bleeding can be concerning, but there are options to help manage it. 🧡 It’s always good to talk to a doctor to make sure everything's okay."
    else:
        return "You're doing great by paying attention to your health. 💪 Keep going, and don't hesitate to ask for help if you need it!"

# Fonction pour prédire la réponse
def predict(name, age, sex, message, history, symptoms):
    messages = [{"role": "system", "content": DEFAULT_PROMPT2}]
    for human, assistant in history:
        messages.append({"role": "user", "content": human})
        messages.append({"role": "assistant", "content": assistant})

    selected_symptoms = ", ".join([sym for sym in symptoms if sym])
    full_message = f"Name: {name}, Age: {age}, Sex: {sex}\nSymptoms: {selected_symptoms}\nAdditional information: {message}"
    messages.append({"role": "user", "content": full_message})

    try:
        completion = client.chat.completions.create(
            model="meta/llama-3.1-8b-instruct",
            messages=messages,
            temperature=0.2,
            top_p=0.9,
            max_tokens=1024,
            stream=True
        )

        full_response = ""
        for chunk in completion:
            if chunk.choices[0].delta.content is not None:
                full_response += chunk.choices[0].delta.content

        return full_response
    except Exception as e:
        return f"Erreur : {str(e)}"

# Point de départ pour lancer l'application FastAPI avec Uvicorn
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)