feat: enhance configuration loading and add image processing endpoints

Author: npv2k1

src/common/config.py

@@ -31,6 +31,9 @@ def from_env(cls) -> "AppConfig":
         )
 
 
+print("AppConfig loaded from environment variables:", AppConfig.from_env())
+
+
 def load_json_config(path: Path) -> dict[str, Any]:
     """Load configuration from JSON file.
 

src/modules/api/app.py

@@ -1,7 +1,12 @@
 """FastAPI application factory."""
 
+from typing import List
 from fastapi import FastAPI, WebSocket, WebSocketDisconnect
 from fastapi.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from fastapi.responses import HTMLResponse
+from fastapi import FastAPI, WebSocket, WebSocketDisconnect, Request
 
 from .routes import v1_router
 
@@ -21,6 +26,7 @@ async def broadcast(self, message: str):
             await connection.send_text(message)
 
 manager = ConnectionManager()
+templates = Jinja2Templates(directory="templates")
 
 def create_app() -> FastAPI:
     """Create and configure the FastAPI application."""
@@ -35,6 +41,8 @@ def create_app() -> FastAPI:
         allow_headers=["*"],
     )
 
+    app.mount("/static", StaticFiles(directory="static"), name="static")
+
     # Include routers
     app.include_router(v1_router)
 
@@ -60,6 +68,11 @@ async def websocket_endpoint(websocket: WebSocket):
             manager.disconnect(websocket)
             await manager.broadcast("A client disconnected")
 
+
+    @app.get("/room/{room_name}", response_class=HTMLResponse)
+    async def broadcaster(request: Request, room_name: str):
+        return templates.TemplateResponse("broadcaster.html", {"request": request, "room_name": room_name})
+
     return app
 
 

src/modules/api/routes/v1.py

@@ -1,24 +1,29 @@
 """API v1 route handlers."""
 
-from fastapi import APIRouter, Response
+from fastapi import APIRouter, Response, File, UploadFile
+import numpy as np
+from fastapi.responses import StreamingResponse
 from typing import Dict, List
-from src.modules.transporter import publish_message
+from src.modules.transporter import add_to_queue
 from src.modules.models.index import embed_text
 import io
 # Create v1 router
 router = APIRouter(prefix='/v1', tags=['v1'])
 import matplotlib.pyplot as plt
+from src.modules.transporter.redis_client import pubsub
+from src.modules.transporter.kafka import kafka_pubsub
 
 @router.get("/hello")
 async def hello_world():
     """Hello world endpoint."""
-    publish_message("hello-python", "Hello from FastAPI!")
-    embed = embed_text("Hello, world!")
-    print(f"Generated embedding: {embed}")
-    return {"message": "Hello, reloaded!", "embedding": list(embed)}
+    add_to_queue("hello-python", "Hello from FastAPI!")
+    await pubsub.publish('chat', "message")
+    await kafka_pubsub.publish('chat', "messagejbdjchsjdhcjsdchbsjdch")
+    return {"message": "Hello, reloaded!"}
 
 @router.get("/health")
-async def health_check() -> Dict[str, str]:
+async def health_check(msg) -> Dict[str, str]:
+    print("[chat rehealthdis] Received: message")
     """Health check endpoint."""
     return {"status": "healthy"}
 
@@ -49,4 +54,58 @@ async def get_plot():
     buf.seek(0)
 
     # Trả về dưới dạng ảnh PNG
-    return Response(content=buf.getvalue(), media_type="image/png")
+    return Response(content=buf.getvalue(), media_type="image/png")
+
+import cv2
+@router.post("/edit-image/")
+async def edit_image(file: UploadFile = File(...)):
+    import cv2
+    # Đọc file ảnh từ request
+    contents = await file.read()
+    nparr = np.frombuffer(contents, np.uint8)
+    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+    
+    # Xử lý ảnh: ví dụ chuyển sang ảnh xám
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    
+    # Chuyển lại thành ảnh màu để trả về (nếu cần)
+    result = cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)
+    
+    # Encode ảnh thành bytes
+    _, img_encoded = cv2.imencode('.jpg', result)
+    return StreamingResponse(io.BytesIO(img_encoded.tobytes()), media_type="image/jpeg")
+
+
+    # Load bộ phân loại khuôn mặt Haar Cascade
+face_cascade = cv2.CascadeClassifier(
+    cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
+)
+
+@router.post("/detect-faces/")
+async def detect_faces(file: UploadFile = File(...)):
+    # Đọc dữ liệu ảnh
+    contents = await file.read()
+    nparr = np.frombuffer(contents, np.uint8)
+    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+    
+    # Chuyển sang ảnh xám để nhận diện
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    
+    # Phát hiện khuôn mặt
+    faces = face_cascade.detectMultiScale(
+        gray,
+        scaleFactor=1.1,
+        minNeighbors=5,
+        minSize=(30, 30)
+    )
+    
+    # Vẽ hình chữ nhật quanh khuôn mặt
+    for (x, y, w, h) in faces:
+        cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)
+    
+    # Chuyển ảnh kết quả thành bytes
+    _, img_encoded = cv2.imencode('.jpg', img)
+    return StreamingResponse(
+        io.BytesIO(img_encoded.tobytes()), 
+        media_type="image/jpeg"
+    )