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
+import cv2
+import numpy as np
+from transformers import pipeline
+import requests
+from io import BytesIO
+import os
+
+model = YOLO('corn20epoch.pt')
+name = ['Corn Rust','Grey Leaf Spot','Leaf Blight']
+# image_directory = "/home/user/app/image"
+# video_directory = "/home/user/app/video"
+
+# 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 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)
+                           
+    text = ""
+    name_weap = ""
+    
+    box = results[0].boxes
+
+    for r in results:
+        im_array = r.plot()
+        im = Image.fromarray(im_array[..., ::-1])
+
+    
+    
+    for r in results:
+        conf = np.array(r.boxes.conf.cpu())
+        cls = np.array(r.boxes.cls.cpu())
+        cls = cls.astype(int)
+        xywh = np.array(r.boxes.xywh.cpu())
+        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")
+            
+                
+      
+    
+    # xywh = int(results.boxes.xywh)
+    # x = xywh[0]
+    # y = xywh[1]
+           
+    return im, text
+
+
+inputs = [
+    gr.Image(type="pil",  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="pil", label="Output Image"),
+           gr.Textbox(label="Result")
+          ]
+
+# examples = [[os.path.join(image_directory, "th (5).jpg"),640, 0.3, 0.6],
+#             [os.path.join(image_directory, "th (8).jpg"),640, 0.3, 0.6],
+#             [os.path.join(image_directory, "th (11).jpg"),640, 0.3, 0.6],
+#             [os.path.join(image_directory, "th (3).jpg"),640, 0.3, 0.6],
+#             [os.path.join(image_directory, "th.jpg"),640, 0.3, 0.6]
+#            ]
+title = """Corn Deseases Detection Finetuned YOLOv8
+<br></br>
+    <a href="https://colab.research.google.com/drive/1ittrxr--vJeRqJquZyNfo7dlq6xRADox?authuser=4">
+        <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Colab" style="display:inline-block;">
+    </a> """
+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.'
+
+
+def pil_to_cv2(pil_image):
+    open_cv_image = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)
+    return open_cv_image
+
+
+def process_video(video_path):
+    cap = cv2.VideoCapture(video_path)
+    
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        
+        pil_img = Image.fromarray(frame[..., ::-1])  
+        result = model.predict(source=pil_img)
+        for r in result:
+            im_array = r.plot()
+            processed_frame = Image.fromarray(im_array[..., ::-1])  
+        yield processed_frame
+    cap.release()
+
+
+video_iface = gr.Interface(
+    fn=process_video,
+    inputs=[
+        gr.Video(label="Upload Video", interactive=True)
+    ],
+    outputs=gr.Image(type="pil",label="Result"),
+    title=title,
+    description="Upload video for inference.",
+    # examples=[[os.path.join(video_directory, "ExampleRifle.mp4")],
+    #     [os.path.join(video_directory, "Knife.mp4")],
+    # ]
+)
+
+
+image_iface = gr.Interface(fn=response2, inputs=inputs, outputs=outputs, 
+                           # examples=examples
+                            title=title, description=description)
+
+demo = gr.TabbedInterface([image_iface, video_iface], ["Image Inference", "Video Inference"])
+
+if __name__ == '__main__':
+    demo.launch()