app.py

import os
import time
import logging
from typing import Optional

# =============================
# Hugging Face cache fix for Spaces
# =============================
os.environ["TRANSFORMERS_CACHE"] = "/tmp/.cache/huggingface/transformers"
os.environ["HF_HOME"] = "/tmp/.cache/huggingface"
os.makedirs("/tmp/.cache/huggingface/transformers", exist_ok=True)

# =============================
# Imports
# =============================
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import pipeline

# =============================
# Logging
# =============================
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("biogpt_chatbot")

# =============================
# PROMPT TEMPLATES
# =============================
MEDICAL_PROMPTS = {
    "dermatology": """
You are DermX-AI, a specialized medical AI assistant trained in dermatology. 
Your role is to provide clear, evidence-based information about skin conditions, 
diagnostic insights, and treatment options. 
- Use simple but professional language, suitable for both patients and clinicians.  
- When explaining, balance medical accuracy with user-friendly clarity.  
- For any uncertain or critical cases, clearly advise consultation with a dermatologist.  
- Always include safety reminders and disclaimers.  
""",
    "general": """
You are a medical AI assistant designed to provide helpful, evidence-based health information.  
When answering:  
- Ensure accuracy and clarity in medical explanations.  
- Provide actionable lifestyle and preventive care suggestions where applicable.  
- Avoid giving definitive diagnoses or prescriptions—always emphasize professional medical consultation.  
- Be empathetic, supportive, and professional in tone.  
""",
    "disclaimer": """
⚠️ Important: I am an AI medical assistant, not a licensed healthcare professional. 
The information provided is for educational purposes only and should not be 
considered a substitute for professional medical advice, diagnosis, or treatment.  
Please consult a dermatologist or qualified healthcare provider for personalized care.  
""",
}

# =============================
# FastAPI setup
# =============================
class ChatRequest(BaseModel):
    question: str
    context: Optional[str] = None
    mode: Optional[str] = "dermatology"  # "dermatology" | "general"
    max_new_tokens: Optional[int] = 100
    temperature: Optional[float] = 0.7
    top_p: Optional[float] = 0.9

class ChatResponse(BaseModel):
    answer: str
    model: str
    took_seconds: float
    confidence: int
    sources: list

app = FastAPI(title="BioGPT-Large Medical Chatbot")

MODEL_ID = os.environ.get("MODEL_ID", "microsoft/BioGPT-Large")
generator = None

# =============================
# Load model on startup
# =============================
@app.on_event("startup")
def load_model():
    global generator
    try:
        logger.info(f"Loading Hugging Face model via pipeline: {MODEL_ID}")
        generator = pipeline("text-generation", model=MODEL_ID, device=-1)
        logger.info("Model loaded successfully.")
    except Exception as e:
        logger.exception("Failed to load model")
        generator = None

# =============================
# Root endpoint
# =============================
@app.get("/")
def root():
    return {"status": "ok", "model_loaded": generator is not None, "model": MODEL_ID}

# =============================
# Chat endpoint
# =============================
@app.post("/chat", response_model=ChatResponse)
def chat(req: ChatRequest):
    if generator is None:
        raise HTTPException(status_code=500, detail="Model not available.")

    if not req.question.strip():
        raise HTTPException(status_code=400, detail="Question cannot be empty")

    # Select system prompt
    mode = req.mode.lower() if req.mode else "dermatology"
    system_prompt = MEDICAL_PROMPTS.get(mode, MEDICAL_PROMPTS["general"])

    # Build final prompt
    prompt = f"{system_prompt}\n\nUser Question: {req.question.strip()}\n\nAI Answer:"
    if req.context:
        prompt = req.context.strip() + "\n\n" + prompt

    logger.info(f"Generating answer for question: {req.question[:80]}...")
    t0 = time.time()

    try:
        outputs = generator(
            prompt,
            max_new_tokens=req.max_new_tokens,
            temperature=req.temperature,
            top_p=req.top_p,
            do_sample=True,
            return_full_text=False,
            num_return_sequences=1,
        )

        answer = outputs[0]["generated_text"].strip()
        final_answer = f"{answer}\n\n{MEDICAL_PROMPTS['disclaimer']}"

        took = time.time() - t0
        confidence = min(95, 70 + int(len(answer) / 50))

        return ChatResponse(
            answer=final_answer,
            model=MODEL_ID,
            took_seconds=round(took, 2),
            confidence=confidence,
            sources=["HuggingFace", MODEL_ID],
        )
    except Exception as e:
        logger.exception("Generation failed")
        raise HTTPException(status_code=500, detail=str(e))