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DOCUMENTATION%20.md

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+#  Smart Statue Detector API - Documentation
+
+## Overview
+
+REST API built with **FastAPI** + **YOLOv8** for detecting **persons** and **Egyptian statues** in images.
+
+- 🟢 **Green box** → Person
+- 🔴 **Red box** → Statue (with real name)
+
+**Base URL:**
+```
+https://morefaat69-smart-statue-detector.hf.space
+```
+
+---
+
+## Endpoints
+
+### `GET /`
+Health check - تأكد إن الـ API شغال.
+
+**Response:**
+```json
+{
+  "message": "AI API is running "
+}
+```
+
+---
+
+### `POST /predict-image`
+Upload an image → get detections + annotated output image.
+
+#### Request
+| Field | Type | Description |
+|-------|------|-------------|
+| `file` | `multipart/form-data` | Image file (jpg, jpeg, png) |
+
+#### Response
+| Field | Type | Description |
+|-------|------|-------------|
+| `total_count` | `int` | Total detections (persons + statues) |
+| `persons` | `int` | Number of persons detected |
+| `statues` | `int` | Number of statues detected |
+| `output_image_url` | `string` | URL of annotated image (temporary) |
+| `output_image_base64` | `string` | Base64 encoded annotated image |
+| `detections` | `array` | List of all detections |
+
+#### Detection Object
+| Field | Type | Description |
+|-------|------|-------------|
+| `type` | `string` | `"person"` or `"statue"` |
+| `name` | `string` | `"Person"` or statue name (e.g. `"Hathor Capital"`) |
+| `confidence` | `float` | Confidence score (0.0 → 1.0) |
+| `bbox` | `array` | Bounding box `[x1, y1, x2, y2]` |
+
+#### Example Response
+```json
+{
+  "total_count": 2,
+  "persons": 1,
+  "statues": 1,
+  "output_image_url": "https://morefaat69-smart-statue-detector.hf.space/uploads/output_test.jpeg",
+  "output_image_base64": "data:image/jpeg;base64,/9j/4AAQSkZJRgAB...",
+  "detections": [
+    {
+      "type": "person",
+      "name": "Person",
+      "confidence": 0.9128,
+      "bbox": [56, 399, 577, 892]
+    },
+    {
+      "type": "statue",
+      "name": "Hathor Capital",
+      "confidence": 0.3147,
+      "bbox": [329, 0, 683, 721]
+    }
+  ]
+}
+```
+
+---
+
+## How to Connect (Integration Examples)
+
+### cURL
+```bash
+curl -X POST "https://morefaat69-smart-statue-detector.hf.space/predict-image" \
+  -H "accept: application/json" \
+  -F "file=@your_image.jpg;type=image/jpeg"
+```
+
+---
+
+### Python
+```python
+import requests
+import base64
+from PIL import Image
+from io import BytesIO
+
+url = "https://morefaat69-smart-statue-detector.hf.space/predict-image"
+
+with open("your_image.jpg", "rb") as f:
+    response = requests.post(url, files={"file": f})
+
+data = response.json()
+
+print(f"Persons: {data['persons']}")
+print(f"Statues: {data['statues']}")
+
+for d in data["detections"]:
+    print(f"{d['type']} → {d['name']} ({d['confidence']:.2%})")
+
+# عرض الصورة الناتجة
+img_data = base64.b64decode(data["output_image_base64"].split(",")[1])
+img = Image.open(BytesIO(img_data))
+img.show()
+```
+
+---
+
+### JavaScript / React
+```javascript
+const detectObjects = async (imageFile) => {
+  const formData = new FormData();
+  formData.append("file", imageFile);
+
+  const response = await fetch(
+    "https://morefaat69-smart-statue-detector.hf.space/predict-image",
+    {
+      method: "POST",
+      body: formData,
+    }
+  );
+
+  const data = await response.json();
+
+  console.log(`Persons: ${data.persons}`);
+  console.log(`Statues: ${data.statues}`);
+
+  // عرض الصورة الناتجة مباشرة
+  return (
+    <div>
+      <img src={data.output_image_base64} alt="Detection Result" />
+      {data.detections.map((d, i) => (
+        <p key={i}>
+          {d.type === "statue" ? "🏛️" : "👤"} {d.name} — {(d.confidence * 100).toFixed(1)}%
+        </p>
+      ))}
+    </div>
+  );
+};
+```
+
+---
+
+### Flutter / Dart
+```dart
+import 'dart:convert';
+import 'dart:io';
+import 'package:http/http.dart' as http;
+
+Future<Map<String, dynamic>> detectObjects(File imageFile) async {
+  final uri = Uri.parse(
+    'https://morefaat69-smart-statue-detector.hf.space/predict-image'
+  );
+
+  final request = http.MultipartRequest('POST', uri);
+  request.files.add(
+    await http.MultipartFile.fromPath('file', imageFile.path)
+  );
+
+  final response = await request.send();
+  final body = await response.stream.bytesToString();
+  final data = jsonDecode(body);
+
+  print('Persons: ${data['persons']}');
+  print('Statues: ${data['statues']}');
+
+  // فك الـ Base64 وعرض الصورة
+  final imageBytes = base64Decode(
+    data['output_image_base64'].split(',')[1]
+  );
+
+  return data;
+}
+
+// عرض الصورة في Flutter
+Image.memory(imageBytes)
+```
+
+---
+
+## Models
+
+| Model | Purpose | Classes |
+|-------|---------|---------|
+| `yolov8n.pt` | Person detection | 1 class: Person |
+| `best.pt` | Egyptian statue detection | 84 classes |
+
+### Supported Statues (84 classes)
+The model can identify statues including:
+`Akhenaten`, `Amenhotep III`, `Nefertiti`, `Sphinx`, `Mask of Tutankhamun`,
+`Great Pyramids of Giza`, `Colossal Statue of Ramesses II`, `Hathor Capital`,
+`Seated Statue of Djoser`, `Statue of Khafre` ... and 74 more.
+
+---
+
+## Configuration
+
+| Parameter | Value | Description |
+|-----------|-------|-------------|
+| `CONF_THRESHOLD` | `0.25` | Minimum confidence to show detection |
+| Person box color | 🟢 Green `(0,255,0)` | BGR format |
+| Statue box color | 🔴 Red `(0,0,255)` | BGR format |
+| Max image size | No limit | Processed as-is |
+
+---
+
+## Interactive Docs
+Swagger UI available at:
+```
+https://morefaat69-smart-statue-detector.hf.space/docs
+```

app .py

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+from fastapi import FastAPI, APIRouter, UploadFile, File, Request
+from fastapi.staticfiles import StaticFiles
+import shutil
+import os
+import cv2
+import base64
+from ultralytics import YOLO
+# ─── Load Models 
+person_model = YOLO("yolov8n.pt")
+statue_model = YOLO("best.pt")
+# ─── App Setup 
+app = FastAPI()
+UPLOAD_FOLDER = "/tmp/uploads"
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+app.mount("/uploads", StaticFiles(directory=UPLOAD_FOLDER), name="uploads")
+CONF_THRESHOLD = 0.30
+router = APIRouter()
+def draw_label(image, label, x1, y1, x2, y2, box_color, text_color):
+    
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    font_scale = 0.6
+    thickness = 2
+    (tw, th), _ = cv2.getTextSize(label, font, font_scale, thickness)
+    
+    if y1 - th - 8 >= 0:
+        label_y1 = y1 - th - 8
+        label_y2 = y1
+        text_y = y1 - 5
+    else:
+        
+        label_y1 = y1
+        label_y2 = y1 + th + 8
+        text_y = y1 + th + 3
+    cv2.rectangle(image, (x1, label_y1), (x1 + tw + 4, label_y2), box_color, -1)
+    cv2.putText(image, label, (x1 + 2, text_y), font, font_scale, text_color, thickness)
+@app.get("/")
+def root():
+    return {"message": "AI API is running 🚀"}
+@router.post("/predict-image")
+async def predict_image(
+    request: Request,
+    file: UploadFile = File(...)
+):
+    safe_filename = file.filename.replace(" ", "_")
+    file_path = os.path.join(UPLOAD_FOLDER, safe_filename)
+    with open(file_path, "wb") as buffer:
+        shutil.copyfileobj(file.file, buffer)
+    image = cv2.imread(file_path)
+    if image is None:
+        return {"error": "Invalid image"}
+    detections = []
+    person_count = 0
+    statue_count = 0
+    # ── Person Detection 
+    person_results = person_model(file_path)
+    for box in person_results[0].boxes:
+        cls_id = int(box.cls)
+        if cls_id != 0:
+            continue
+        conf = float(box.conf)
+        if conf < CONF_THRESHOLD:
+            continue
+        x1, y1, x2, y2 = map(int, box.xyxy[0])
+        cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        draw_label(image, f"Person {conf:.2f}", x1, y1, x2, y2,
+                   box_color=(0, 255, 0), text_color=(0, 0, 0))
+        detections.append({
+            "type": "person",
+            "name": "Person",
+            "confidence": round(conf, 4),
+            "bbox": [x1, y1, x2, y2]
+        })
+        person_count += 1
+    # ── Statue Detection 
+    statue_results = statue_model(file_path)
+    for box in statue_results[0].boxes:
+        conf = float(box.conf)
+        if conf < CONF_THRESHOLD:
+            continue
+        cls_id = int(box.cls)
+        statue_name = statue_results[0].names[cls_id]
+        x1, y1, x2, y2 = map(int, box.xyxy[0])
+        cv2.rectangle(image, (x1, y1), (x2, y2), (0, 0, 255), 2)
+        draw_label(image, f"{statue_name} {conf:.2f}", x1, y1, x2, y2,
+                   box_color=(0, 0, 255), text_color=(255, 255, 255))
+        detections.append({
+            "type": "statue",
+            "name": statue_name,
+            "confidence": round(conf, 4),
+            "bbox": [x1, y1, x2, y2]
+        })
+        statue_count += 1
+    # ── Save Output Image 
+    output_filename = f"output_{safe_filename}"
+    output_path = os.path.join(UPLOAD_FOLDER, output_filename)
+    cv2.imwrite(output_path, image)
+    with open(output_path, "rb") as img_file:
+        image_base64 = base64.b64encode(img_file.read()).decode("utf-8")
+    image_url = f"{request.base_url}uploads/{output_filename}"
+    return {
+        "total_count": len(detections),
+        "persons": person_count,
+        "statues": statue_count,
+        "output_image_url": image_url,
+        "output_image_base64": f"data:image/jpeg;base64,{image_base64}",
+        "detections": detections
+    }
+app.include_router(router)
+