Create app.py

app.py

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+import gradio as gr
+import matplotlib.pyplot as plt
+from PIL import Image
+from ultralyticsplus import YOLO, render_result
+import cv2
+import numpy as np
+from transformers import pipeline
+
+model = YOLO('best (1).pt')
+model2 = pipeline('image-classification','Kaludi/csgo-weapon-classification')
+name = ['grenade','knife','pistol','rifle']
+
+ # for i, r in enumerate(results):
+      
+ #    # Plot results image
+ #      im_bgr = r.plot()  
+ #      im_rgb = im_bgr[..., ::-1]  # Convert BGR to RGB
+
+def response(image):
+  print(image)
+  results = model(image)
+  text = ""
+  name_weap = ""
+    
+  for r in results:
+    conf = np.array(r.boxes.conf)
+    cls = np.array(r.boxes.cls)
+    cls = cls.astype(int)
+    xywh = np.array(r.boxes.xywh)
+    xywh = xywh.astype(int)  
+      
+    for con, cl, xy in zip(conf, cls, xywh):
+        cone = con.astype(float)
+        conef = round(cone,3)
+        conef = conef * 100
+        text += (f"Detected {name[cl]} with confidence {round(conef,1)}% at ({xy[0]},{xy[1]})\n")
+        
+        if cl == 0:
+            name_weap += name[cl] + '\n'
+        elif cl == 1:
+            name_weap += name[cl] + '\n'
+        elif cl == 2:
+            out = model2(image)
+            name_weap += out[0]["label"] + '\n'
+        elif cl == 3:
+            out = model2(image)
+            name_weap += out[0]["label"] + '\n'
+
+        
+    # im_rgb = Image.fromarray(im_rgb)
+    
+      
+    return name_weap, text
+
+
+
+def response2(image: gr.Image = None,image_size: gr.Slider = 640, conf_threshold: gr.Slider = 0.3, iou_threshold: gr.Slider = 0.6):
+
+    results = model.predict(image, conf=conf_threshold, iou=iou_threshold, imgsz=image_size)
+
+    box = results[0].boxes
+
+    render = render_result(model=model, image=image, result=results[0], rect_th = 1, text_th = 1)
+   
+   
+    weapon_name, text_detection = response(image)
+   
+    
+    # xywh = int(results.boxes.xywh)
+    # x = xywh[0]
+    # y = xywh[1]
+        
+   
+            
+    return render, text_detection, weapon_name
+
+
+inputs = [
+    gr.Image(type="filepath",  label="Input Image"),
+    gr.Slider(minimum=320, maximum=1280, value=640,
+                     step=32, label="Image Size"),
+    gr.Slider(minimum=0.0, maximum=1.0, value=0.3,
+                     step=0.05, label="Confidence Threshold"),
+    gr.Slider(minimum=0.0, maximum=1.0, value=0.6,
+                     step=0.05, label="IOU Threshold"),
+]
+
+
+outputs = [gr.Image( type="filepath", label="Output Image"),
+           gr.Textbox(label="Result"),
+           gr.Textbox(label="Weapon Name")
+          ]
+
+
+# examples = [['th (11).jpg', 640, 0.3, 0.6],
+#             ['th (8).jpg', 640, 0.3, 0.6],
+#             ['th (3).jpg', 640, 0.3, 0.6],
+#             ['th.jpg', 640, 0.15, 0.6]
+#            ]
+           
+           
+
+
+iface = gr.Interface(fn=response2, inputs=inputs, outputs=outputs)
+iface.launch(debug=True)