Update app.py

app.py

@@ -1,129 +1,3 @@
-# old code
-# import gradio as gr
-# import torch
-
-# model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt')
-# Define the face detector function 
-# def detect_faces(image):
-#    # Loading in yolov5s - you can switch to larger models such as yolov5m or yolov5l, or smaller such as yolov5n
-#     results = model(image)
-
-#     return results.render()[0]
-
-# # Create a Gradio interface
-# iface = gr.Interface(fn=detect_faces, inputs=gr.Image(source="webcam", tool =None), outputs="image")
-
-# # Launch the interface
-# iface.launch(debug=True)
-
-# demo = gr.TabbedInterface([img_demo, vid_demo], ["Image", "Video"])
-
-# if __name__ == "__main__":
-#     demo.launch()
-# from IPython.display import clear_output
-# import os, urllib.request
-# import subprocess
-# from roboflow import Roboflow
-# import json
-# from time import sleep
-# from PIL import Image, ImageDraw
-# import io
-# import base64
-# import requests
-# from os.path import exists
-# import sys, re, glob
-
-# model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt')
-# rf = Roboflow(api_key="affmrRA3zyr34kAQF3sJ")
-# project = rf.workspace().project("ecosmart-pxc0t")
-# dataset = project.version(4).model
-
-# def detect_video(video):
-#     HOME = os.path.expanduser("~")
-#     pathDoneCMD = f'{HOME}/doneCMD.sh'
-#     if not os.path.exists(f"{HOME}/.ipython/ttmg.py"):
-#         hCode = "https://raw.githubusercontent.com/yunooooo/gcct/master/res/ttmg.py"
-#         urllib.request.urlretrieve(hCode, f"{HOME}/.ipython/ttmg.py")
-    
-#     from ttmg import (
-#         loadingAn,
-#         textAn,
-#     )
-
-#     os.chdir("/content/")
-#     os.makedirs("videos_to_infer", exist_ok=True)
-#     os.makedirs("inferred_videos", exist_ok=True)
-#     os.chdir("videos_to_infer")
-#     os.environ['inputFile'] = video.name
-#     command = ['ffmpeg', '-hide_banner', '-loglevel', 'error', '-i', input_file, '-vf', 'fps=2', output_pattern]
-#     subprocess.run(command)
-    
-#     subprocess.run(['pip', 'install', 'roboflow'])
-#     install_roboflow()
-#     model = version.model
-#     print(model)
-
-#     file_path = "/content/videos_to_infer/"
-#     extention = ".png"
-#     globbed_files = sorted(glob.glob(file_path + '*' + extention))
-#     print(globbed_files)
-#     for image in globbed_files:
-#     # INFERENCE
-#     predictions = model.predict(image).json()['predictions']
-#     newly_rendered_image = Image.open(image)
-    
-#       # RENDER
-#       # for each detection, create a crop and convert into CLIP encoding
-#     print(predictions)
-#     for prediction in predictions:
-#           # rip bounding box coordinates from current detection
-#           # note: infer returns center points of box as (x,y) and width, height
-#           # ----- but pillow crop requires the top left and bottom right points to crop
-#         x0 = prediction['x'] - prediction['width'] / 2
-#         x1 = prediction['x'] + prediction['width'] / 2
-#         y0 = prediction['y'] - prediction['height'] / 2
-#         y1 = prediction['y'] + prediction['height'] / 2
-#         box = (x0, y0, x1, y1)
-    
-#         newly_rendered_image = draw_boxes(box, x0, y0, newly_rendered_image, prediction['class'])
-    
-#       # WRITE
-#     save_with_bbox_renders(newly_rendered_image)
-
-#     # Run ffmpeg command
-#     subprocess.run(['ffmpeg', '-r', '8', '-s', '1920x1080', '-i', '/content/inferred_videos/YOUR_VIDEO_FILE_out%04d.png', '-vcodec', 'libx264', '-crf', '25', '-pix_fmt', 'yuv420p', 'test.mp4'])
-#     # Call the function to execute the commands
-#     execute_commands()
-
-
-    
-# def draw_boxes(box, x0, y0, img, class_name):
-#     bbox = ImageDraw.Draw(img)
-
-#     bbox.rectangle(box, outline =color_map[class_name], width=5)
-#     bbox.text((x0, y0), class_name, fill='black', anchor='mm')
-
-#     return img
-
-# def save_with_bbox_renders(img):
-#     file_name = os.path.basename(img.filename)
-#     img.save('/content/inferred_videos/' + file_name)
-
-
-    
-# loadingAn(name="lds")
-# textAn("Installing Dependencies...", ty='twg')
-# os.system('pip install git+git://github.com/AWConant/jikanpy.git')
-# os.system('add-apt-repository -y ppa:jonathonf/ffmpeg-4')
-# os.system('apt-get update')
-# os.system('apt install mediainfo')
-# os.system('apt-get install ffmpeg')
-# clear_output()
-# print('Installation finished.')
-
-# Define the face detector function
-
-
 import gradio as gr
 import torch
 import cv2
@@ -221,8 +95,12 @@ vid_interface = gr.Interface(
     outputs="video",
     title="Video"
 )
+
+# Add examples
+examples1 = ['potatoleaf.jpg','potatoearlyblight.jpg','potatolate.jpg']
+
 # Create a list of interfaces
-interfaces = [img_interface, vid_interface]
+interfaces = [img_interface, examples1, vid_interface, examples2]
 
 # Create the tabbed interface
 tabbed_interface = gr.TabbedInterface(interfaces, ["Image", "Video"])