Delete app.py

app.py

@@ -1,52 +0,0 @@
-import gradio as gr
-import torch
-import cv2
-import numpy as np
-from PIL import Image
-from ultralytics import YOLO
-
-# Load YOLOv11 Model
-model_path = "best.pt" 
-model = YOLO(model_path)
-
-def predict(image):
-    image = np.array(image)
-    results = model(image, conf=0.85)
-
-    detected_classes = set()  # Track unique detected classes
-    labels = []
-
-    # Draw bounding boxes and extract labels
-    for result in results:
-        for box in result.boxes:
-            x1, y1, x2, y2 = map(int, box.xyxy[0])
-            conf = box.conf[0]
-            cls = int(box.cls[0])
-            class_name = model.names[cls]
-
-            detected_classes.add(class_name)  # Store detected class
-            label = f"{class_name} {conf:.2f}"
-            labels.append(label)
-
-            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
-            cv2.putText(image, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
-
-    # Define possible classes (adjust based on your dataset)
-    possible_classes = {"front", "back"}
-
-    # Identify missing class if any
-    missing_classes = possible_classes - detected_classes
-    if missing_classes:
-        labels.append(f"Missing: {', '.join(missing_classes)}")
-
-    return Image.fromarray(image), labels
-
-# Gradio Interface
-iface = gr.Interface(
-    fn=predict, 
-    inputs="image", 
-    outputs=["image", "text"],  # Returning both image and detected labels
-    title="YOLOv11 Object Detection (Front & Back Card)"
-)
-
-iface.launch()