added description for sliders

app.py

@@ -71,8 +71,8 @@ with gr.Blocks(title="Yolo V2 Object detection") as app:
             with gr.Row():
                 radio_btn=gr.Radio(['upload','webcam'],value='upload',interactive=True)
 
-            conf_slider=gr.Slider(0,1,value=0.2,label='min_confidence',interactive=True)
-            nms_slider=gr.Slider(0,1,value=0.3,label='nms_iou_threshold',interactive=True)
+            conf_slider=gr.Slider(0,1,value=0.2,label='min_confidence(removes predictions with confidence lower than the min_confidence)',interactive=True)
+            nms_slider=gr.Slider(0,1,value=0.3,label='nms_iou_threshold(lower the value removes more overlapping boxes)',interactive=True)
             
             with gr.Row():
                 reset_btn=gr.Button('reset',visible=False)

inference.py

@@ -0,0 +1,64 @@
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.patches import Rectangle
+from load_model import load_model
+from decode_yolo_v2 import *
+from config import *
+import gradio as gr
+import json
+from glob import glob
+
+
+# objs_found=[{'p': float(0.9001910090446472), 'xywh': [132, 122, 72, 156], 'class_idx': 14, 'class': 'person'}, {'p': float(0.9001910090446472), 'xywh': [260, 126, 157, 284], 'class_idx': 14, 'class': 'person'}]
+# print(objs_found)
+# print(json.dumps(objs_found))
+
+model=load_model("yolo_v2(iou_70.5945).h5")
+
+
+
+def get_preds(test_img,p=0.2,iou_threshold=0.3):
+    img=cv2.resize(cv2.cvtColor(cv2.imread(test_img),cv2.COLOR_BGR2RGB),[image_size,image_size])
+    img=np.expand_dims(img,axis=0)
+    y_pred=model.predict(img)
+
+
+    objs_found=get_objects(y_pred[0],p=p)
+    objs_found=nms(objs_found,iou_threshold=iou_threshold)
+    print("objs_found:",objs_found)
+    plt.axis('off')
+    # show_objects(img[0],objs_found)
+    img=pred_image(img[0],objs_found)
+    plt.imshow(img)
+    plt.show()
+
+# get_preds(input("Enter image path:"))
+# get_preds("C:/Users/Home/Downloads/image_2.jpeg")
+
+def get_output(img,p,iou_threshold):
+    img=cv2.resize(img,[image_size,image_size])
+    img=np.expand_dims(img,axis=0)
+    y_pred=model.predict(img,verbose=0)
+
+
+    objs_found=get_objects(y_pred[0],p=p)
+    objs_found=nms(objs_found,iou_threshold=iou_threshold)
+    # print("objs_found:",objs_found)
+    img=pred_image(img[0],objs_found)
+    return img,json.dumps({'objects_found':objs_found})
+
+app=gr.Interface(get_output,inputs=[
+                                    gr.Image(),
+                                    # gr.Image(streaming=True,source='webcam'),
+                                    gr.Slider(0,1,value=0.2,label='min_confidence'),
+                                    gr.Slider(0,1,value=0.3,label='nms_iou_threshold')],
+                                    
+                                    outputs=[gr.Image(label='Objects Found'),gr.JSON(label='Objects_found')],
+                                    title="Yolo V2 Object detection",
+                                    description=f"we can detection 20 type of objects which are: {', '.join(class_names)}",
+                                    examples=[[item] for item in glob('examples/*')],
+                                    # live=True
+                                    )
+app.launch()
+# app.launch(share=True)