Update app.py

app.py

@@ -1,41 +1,66 @@
-from fastapi import FastAPI, UploadFile, File
+from fastapi import FastAPI, HTTPException
 import cv2
 import numpy as np
 import uvicorn
-from io import BytesIO
-from PIL import Image
+import base64
+import re
+from pydantic import BaseModel
 
 app = FastAPI()
 
-def find_diff(bg_img, target_img):
-    # Robah ka grayscale
+# Model untuk menerima data Base64 dari PHP
+class CaptchaData(BaseModel):
+    background: str
+    target: str
+
+def solve_distance(bg_b64, target_b64):
+    """
+    Logika AI untuk menghitung jarak geser dengan Canny Edge Detection
+    Sesuai dengan kebutuhan slide puzzle di rs.js
+    """
+    # 1. Decode Base64 ke OpenCV format
+    bg_bytes = base64.b64decode(re.sub(r'^data:image/\w+;base64,', '', bg_b64))
+    target_bytes = base64.b64decode(re.sub(r'^data:image/\w+;base64,', '', target_b64))
+    
+    bg_img = cv2.imdecode(np.frombuffer(bg_bytes, np.uint8), cv2.IMREAD_COLOR)
+    target_img = cv2.imdecode(np.frombuffer(target_bytes, np.uint8), cv2.IMREAD_COLOR)
+    
+    # 2. Preprocessing ke Grayscale
     bg_gray = cv2.cvtColor(bg_img, cv2.COLOR_BGR2GRAY)
     target_gray = cv2.cvtColor(target_img, cv2.COLOR_BGR2GRAY)
     
-    # Template Matching
-    res = cv2.matchTemplate(bg_gray, target_gray, cv2.TM_CCOEFF_NORMED)
+    # 3. Canny Edge Detection (Sangat krusial untuk memisahkan bentuk puzzle)
+    # Ini membantu mengabaikan perbedaan warna/noise di background
+    bg_edge = cv2.Canny(bg_gray, 100, 200)
+    target_edge = cv2.Canny(target_gray, 100, 200)
+    
+    # 4. Template Matching menggunakan hasil Edges
+    res = cv2.matchTemplate(bg_edge, target_edge, cv2.TM_CCOEFF_NORMED)
     _, _, _, max_loc = cv2.minMaxLoc(res)
     
-    # max_loc[0] nyaeta koordinat X (jarak geser)
-    return max_loc[0]
+    # max_loc[0] adalah koordinat X (jarak geser horizontal)
+    return float(max_loc[0])
 
 @app.post("/solve")
-async def solve(background: UploadFile = File(...), target: UploadFile = File(...)):
+async def solve(data: CaptchaData):
     try:
-        # Baca gambar background
-        bg_bytes = await background.read()
-        bg_img = cv2.imdecode(np.frombuffer(bg_bytes, np.uint8), cv2.IMREAD_COLOR)
+        if not data.background or not data.target:
+            raise HTTPException(status_code=400, detail="Data gambar tidak lengkap")
+            
+        distance = solve_distance(data.background, data.target)
         
-        # Baca gambar target (potongan puzzle)
-        target_bytes = await target.read()
-        target_img = cv2.imdecode(np.frombuffer(target_bytes, np.uint8), cv2.IMREAD_COLOR)
-        
-        # Cari jarak
-        distance = find_diff(bg_img, target_img)
-        
-        return {"status": "success", "distance": int(distance)}
+        return {
+            "status": "success",
+            "distance": int(distance),
+            "method": "canny_edge_matching"
+        }
     except Exception as e:
         return {"status": "error", "message": str(e)}
 
+@app.get("/forget")
+async def health_check():
+    return {"status": "online", "description": "RSCaptcha AI Solver"}
+
 if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    # Port 7860 adalah default untuk Hugging Face Spaces
+    uvicorn.run(app, host="0.0.0.0", port=7860)