Initial commit: Recycling object detection project

- Added Fast api web application for recycling object detection
- Included YOLOv8 model training notebook
- Set up project structure with datasets and training directories
- Added requirements.txt for dependencies
- Configured .gitignore for Python virtual environment and cache files

app.py

@@ -0,0 +1,50 @@
+from fastapi import FastAPI, File, UploadFile, Request
+from fastapi.responses import HTMLResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+import uvicorn
+from ultralytics import YOLO
+import cv2
+import numpy as np
+from PIL import Image
+import io
+import os
+import base64
+
+app = FastAPI()
+
+# Initialize templates
+templates = Jinja2Templates(directory="templates")
+
+# Load YOLO model
+current_directory = os.getcwd()
+model_path = current_directory + '\\datasets\\runs\\detect\\train8\\weights\\last.pt'
+model = YOLO(model_path)
+
+@app.get("/", response_class=HTMLResponse)
+async def home(request: Request):
+    return templates.TemplateResponse("index.html", {"request": request})
+
+@app.post("/upload")
+async def upload_image(file: UploadFile = File(...)):
+    # Read the uploaded file
+    contents = await file.read()
+    
+    # Convert to numpy array
+    nparr = np.frombuffer(contents, np.uint8)
+    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+    
+    # Run YOLO prediction
+    results = model(img)
+    
+    # Draw bounding boxes
+    annotated_img = results[0].plot()
+    
+    # Convert to base64
+    _, img_encoded = cv2.imencode('.jpg', annotated_img)
+    img_base64 = base64.b64encode(img_encoded).decode('utf-8')
+    
+    return {"image": img_base64}
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="localhost", port=8000)