app.py

import os
import random
import warnings
import logging
from fastapi import FastAPI, File, UploadFile
from fastapi.middleware.cors import CORSMiddleware
from PIL import Image
import io
import torch
import numpy as np
import cv2
import mediapipe as mp
from transformers import Owlv2Processor, Owlv2ForObjectDetection
from transformers import CLIPProcessor, CLIPModel
from duckduckgo_search import DDGS

# --- AYARLAR ---
warnings.filterwarnings("ignore")
logging.getLogger("transformers").setLevel(logging.ERROR)

app = FastAPI()

# Mobil uygulamadan gelen isteklere izin ver
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# --- MODELLERİ BAŞLATTA YÜKLE ---
print("⏳ Modeller Yükleniyor...")
device = "cpu" # Hugging Face Free Tier CPU kullanır

# OWL-v2
owl_id = "google/owlv2-base-patch16-ensemble"
owl_processor = Owlv2Processor.from_pretrained(owl_id)
owl_model = Owlv2ForObjectDetection.from_pretrained(owl_id).to(device)

# CLIP
clip_id = "openai/clip-vit-base-patch32"
clip_processor = CLIPProcessor.from_pretrained(clip_id)
clip_model = CLIPModel.from_pretrained(clip_id).to(device)

# MediaPipe
mp_face_mesh = mp.solutions.face_mesh
face_mesh = mp_face_mesh.FaceMesh(static_image_mode=True, max_num_faces=1, refine_landmarks=True)

print("✅ Sunucu Hazır!")

# --- SÖZLÜKLER VE YEDEK ÜRÜNLER ---
TR_LABELS = {
    "acne": "Akne", "pimple": "Sivilce", "dark spot": "Leke",
    "wrinkles": "Kırışıklık", "oily skin": "Yağlanma",
    "dry flaky skin": "Kuruluk", "skin redness": "Kızarıklık",
    "peeling skin": "Soyulma", "rough skin": "Pürüzlü"
}

FALLBACK_DATABASE = {
    "Salisilik Asit": [{"title": "La Roche-Posay Effaclar", "link": "https://www.trendyol.com/sr?q=effaclar"}, {"title": "CeraVe SA", "link": "https://www.hepsiburada.com/ara?q=cerave+sa"}],
    "Çay Ağacı": [{"title": "The Body Shop Tea Tree", "link": "https://www.thebodyshop.com.tr"}, {"title": "Sebamed Clear Face", "link": "https://www.gratis.com"}],
    "C Vitamini": [{"title": "Garnier C Vitamini", "link": "https://www.trendyol.com"}, {"title": "La Roche-Posay C10", "link": "https://www.hepsiburada.com"}],
    "Hyaluronik Asit": [{"title": "L'Oreal Hyaluron Uzmanı", "link": "https://www.trendyol.com"}, {"title": "Vichy Mineral 89", "link": "https://www.hepsiburada.com"}],
    "Centella": [{"title": "Dr. Jart+ Cicapair", "link": "https://www.sephora.com.tr"}, {"title": "Missha Cica", "link": "https://www.missha.com.tr"}]
}

# --- FONKSİYONLAR ---
def get_skin_type(image):
    prompts = ["extremely oily shiny skin", "very dry flaky skin", "normal skin", "combination skin"]
    labels = ["YAĞLI", "KURU", "NORMAL", "KARMA"]
    inputs = clip_processor(text=prompts, images=image, return_tensors="pt", padding=True).to(device)
    with torch.no_grad(): probs = clip_model(**inputs).logits_per_image.softmax(dim=1)
    return labels[torch.max(probs, 1).indices.item()]

def get_products(ingredient, skin_type):
    found = []
    # Canlı Arama
    try:
        with DDGS() as ddgs:
            query = f"site:trendyol.com {ingredient} {skin_type} cilt"
            results = list(ddgs.text(query, max_results=2))
            for r in results:
                found.append({"title": r['title'].split("|")[0], "link": r['href'], "source": "Trendyol"})
    except: pass
    
    # Yedek Depo
    if len(found) < 2:
        key_found = None
        for key in FALLBACK_DATABASE:
            if key in ingredient: key_found = key
        if key_found:
            for item in FALLBACK_DATABASE[key_found]:
                found.append({"title": item['title'], "link": item['link'], "source": "Öneri"})
    
    return found[:4]

def generate_prescription(skin_type, issues):
    prescriptions = []
    # Temel
    if skin_type == "YAĞLI": prescriptions.append({"sorun": "Yağlı Cilt Temizliği", "icerik": "Salisilik Asit"})
    elif skin_type == "KURU": prescriptions.append({"sorun": "Kuru Cilt Onarımı", "icerik": "Hyaluronik Asit"})
    
    # Soruna Özel
    unique_issues = list(set(issues))
    for issue in unique_issues:
        if "acne" in issue or "pimple" in issue:
            prescriptions.append({"sorun": "Akne Tedavisi", "icerik": "Çay Ağacı"})
            break
        elif "dark spot" in issue:
            prescriptions.append({"sorun": "Leke Giderici", "icerik": "C Vitamini"})
        elif "redness" in issue:
            prescriptions.append({"sorun": "Kızarıklık Giderici", "icerik": "Centella"})
            
    return prescriptions

# --- API ENDPOINT ---
@app.get("/")
def home():
    return {"status": "Pure Sense API Çalışıyor", "version": "1.0"}

@app.post("/analyze")
async def analyze_skin(file: UploadFile = File(...)):
    contents = await file.read()
    image = Image.open(io.BytesIO(contents)).convert("RGB")
    
    # 1. Cilt Tipi
    skin_type = get_skin_type(image)
    
    # 2. Sorun Tespiti
    text_queries = [["acne", "pimple", "dark spot", "skin redness", "dry flaky skin"]]
    inputs = owl_processor(text=text_queries, images=image, return_tensors="pt").to(device)
    with torch.no_grad(): outputs = owl_model(**inputs)
    
    target_sizes = torch.Tensor([image.size[::-1]])
    results = owl_processor.post_process_object_detection(outputs=outputs, target_sizes=target_sizes, threshold=0.02)[0]
    
    detections = []
    issues_found = []
    
    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
        lbl_en = text_queries[0][label]
        conf = round(score.item() * 100, 1)
        
        if lbl_en in ["acne", "pimple"] and conf < 10: continue
        if lbl_en not in ["acne", "pimple"] and conf < 3: continue
        
        lbl_tr = TR_LABELS.get(lbl_en, lbl_en)
        if lbl_tr not in issues_found: issues_found.append(lbl_tr)
        
        detections.append({
            "label": lbl_tr,
            "confidence": conf,
            "box": [int(i) for i in box.tolist()]
        })
    
    # 3. Reçete
    prescriptions = []
    rx_list = generate_prescription(skin_type, [i.lower() for i in text_queries[0] if TR_LABELS.get(i,i) in issues_found]) # Basit eşleştirme
    
    for rx in rx_list:
        prods = get_products(rx['icerik'], skin_type)
        prescriptions.append({
            "title": rx['sorun'],
            "ingredient": rx['icerik'],
            "products": prods
        })

    return {
        "skin_type": skin_type,
        "detections": detections,
        "prescriptions": prescriptions
    }