import os
import re
import gradio as gr
from huggingface_hub import InferenceClient

HF_TOKEN = os.getenv("HF_TOKEN")

if not HF_TOKEN:
    raise ValueError("HF_TOKEN secret is missing. Add it in your Hugging Face Space settings.")

MODEL_ID = os.getenv("MODEL_ID", "Qwen/Qwen2.5-7B-Instruct:together")

client = InferenceClient(
    provider="auto",
    api_key=HF_TOKEN,
    timeout=120
)

SAFETY_PROMPT = """
You are a health education assistant.

Your role:
- Explain health topics in simple, clear language.
- Provide general health education only.
- Do not diagnose the user.
- Do not prescribe medicines.
- Do not give personalized treatment plans.
- Do not replace a doctor, pharmacist, nurse, or emergency service.
- Encourage users to speak with a qualified health professional.

Medication safety:
- If the user asks about medicines, explain only general information.
- Tell the user to confirm medicine use, dose, interactions, pregnancy safety, allergies, and contraindications with a pharmacist or doctor.

Emergency safety:
- If the user mentions chest pain, difficulty breathing, fainting, severe weakness, confusion, stroke symptoms, seizures, severe bleeding, suicidal thoughts, pregnancy complications, or symptoms in a baby, tell them to seek urgent medical attention immediately.

Response style:
- Be brief.
- Be practical.
- Use plain language.
- Say when you are unsure.
"""

RED_FLAG_PATTERNS = [
    r"chest pain",
    r"difficulty breathing",
    r"shortness of breath",
    r"fainting",
    r"passed out",
    r"loss of consciousness",
    r"severe bleeding",
    r"seizure",
    r"stroke",
    r"face drooping",
    r"weakness on one side",
    r"suicidal",
    r"kill myself",
    r"pregnancy bleeding",
    r"baby.*not breathing",
    r"confusion",
    r"severe weakness"
]

def detect_red_flags(text):
    text = text.lower()
    for pattern in RED_FLAG_PATTERNS:
        if re.search(pattern, text):
            return True
    return False

def urgent_response():
    return (
        "This may be urgent. Please seek emergency medical care immediately or contact your local emergency number. "
        "Do not wait for an online chatbot response. If someone is with you, ask them to help you get medical care now."
    )

def build_messages(user_message, history=None):
    messages = [
        {"role": "system", "content": SAFETY_PROMPT}
    ]

    if history:
        for item in history[-6:]:
            role = item.get("role")
            content = item.get("content")

            if role in ["user", "assistant"] and content:
                messages.append({"role": role, "content": content})

    messages.append({"role": "user", "content": user_message})

    return messages

def generate_answer(user_message, history=None):
    if detect_red_flags(user_message):
        return urgent_response()

    messages = build_messages(user_message, history)

    try:
        response = client.chat.completions.create(
            model=MODEL_ID,
            messages=messages,
            max_tokens=350,
            temperature=0.2
        )

        answer = response.choices[0].message.content
        return answer.strip()

    except Exception as error:
        return (
            "I could not get a response from the hosted model. "
            "Please check your Hugging Face token, model access, or Inference Provider availability. "
            f"Technical error: {error}"
        )

demo = gr.ChatInterface(
    fn=generate_answer,
    title="Hosted Hugging Face Health Education Chatbot",
    description=(
        "A simple health education chatbot using a hosted Hugging Face model. "
        "This demo does not provide diagnosis, prescriptions, or emergency care."
    ),
    examples=[
        "What is diabetes?",
        "What are common symptoms of malaria?",
        "Can I take paracetamol for fever?",
        "I have chest pain and difficulty breathing.",
        "Explain hypertension in simple terms."
    ]
)

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