app.py

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
import requests
from io import BytesIO

model = YOLO('best (1).pt')
model2 = pipeline('image-classification','Kaludi/csgo-weapon-classification')
name = ['grenade','knife','pistol','rifle']

url_example="https://drive.google.com/file/d/1bBq0bNmJ5X83tDWCzdzHSYCdg-aUL4xO/view?usp=drive_link"
url_example='https://drive.google.com/uc?id=' + url_example.split('/')[-2]
r = requests.get(url_example)
im1 = Image.open(BytesIO(r.content))

url_example="https://drive.google.com/file/d/16Z7QzvZ99fbEPj1sls_jOCJBsC0h_dYZ/view?usp=drive_link"
url_example='https://drive.google.com/uc?id=' + url_example.split('/')[-2]
r = requests.get(url_example)
im2 = Image.open(BytesIO(r.content))

url_example="https://drive.google.com/file/d/13mjTMS3eR0AKYSbV-Fpb3fTBno_T42JN/view?usp=drive_link"
url_example='https://drive.google.com/uc?id=' + url_example.split('/')[-2]
r = requests.get(url_example)
im3 = Image.open(BytesIO(r.content))

url_example="https://drive.google.com/file/d/1-XpFsa_nz506Ul6grKElVJDu_Jl3KZIF/view?usp=drive_link"
url_example='https://drive.google.com/uc?id=' + url_example.split('/')[-2]
r = requests.get(url_example)
im4 = Image.open(BytesIO(r.content))
 # 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 = [[im1, 640, 0.3, 0.6],
            [im2, 640, 0.3, 0.6],
            [im3, 640, 0.3, 0.6],
            [im4, 640, 0.15, 0.6]
           ]
           
           
title = 'Weapon Detection Finetuned'

description = 'Image Size: Defines the image size for inference.\nConfidence Treshold: Sets the minimum confidence threshold for detections.\nIOU Treshold: Intersection Over Union (IoU) threshold for Non-Maximum Suppression (NMS). Useful for reducing duplicates.'

iface = gr.Interface(fn=response2, inputs=inputs, outputs=outputs, examples=examples, title=title, description=description)
iface.launch()