Update app.py

app.py

@@ -1,61 +1,29 @@
+
 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('Corn-Disease50epoch.pt')
-name = ['Leaf Blight','Corn Rust','Gray Leaf Spot', 'Healthy']
+name = ['Leaf Blight', 'Corn Rust', 'Gray Leaf Spot', 'Healthy']
 image_directory = "/home/user/app/images"
-# 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):
 
+def response2(image, image_size=640, conf_threshold=0.3, iou_threshold=0.6):
     results = model.predict(image, conf=conf_threshold, iou=iou_threshold, imgsz=image_size)
-                           
+                       
     text = ""
     name_weap = ""
     solution = ""
-    
-    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())
@@ -65,9 +33,9 @@ def response2(image: gr.Image = None,image_size: gr.Slider = 640, conf_threshold
       
         for con, cl, xy in zip(conf, cls, xywh):
             cone = con.astype(float)
-            conef = round(cone,3)
+            conef = round(cone, 3)
             conef = conef * 100
-            text += (f"Detected {name[cl]} with confidence {round(conef,1)}% at ({xy[0]},{xy[1]})\n")
+            text += (f"Detected {name[cl]} with confidence {round(conef, 1)}% at ({xy[0]},{xy[1]})\n")
                 
             if name[cl] == "Corn Rust":
                 solution = (f"{solution} Apply fungicides with active ingredients like propiconazole or tebuconazole when symptoms appear.\n") 
@@ -75,47 +43,13 @@ def response2(image: gr.Image = None,image_size: gr.Slider = 640, conf_threshold
                 solution = (f"{solution} Use fungicides containing strobilurins (e.g., azoxystrobin) or triazoles.\n") 
             elif name[cl] == "Leaf Blight":
                 solution = (f"{solution} Treat with fungicides such as mancozeb or chlorothalonil during the early stages.\n") 
-    
-    # xywh = int(results.boxes.xywh)
-    # x = xywh[0]
-    # y = xywh[1]
            
     return im, text, solution
 
-
-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"), gr.Textbox(label="Solution")
-          ]
-
-examples = [
-    ["/home/user/app/images/jagung7.jpg", 640, 0.3, 0.6],
-    ["/home/user/app/images/jagung4.jpeg", 640, 0.3, 0.6],
-    ["/home/user/app/images/jagung6.jpeg", 640, 0.3, 0.6]
-]
-
-title = """Corn Diseases Detection Finetuned YOLOv11
-<br></br>
-    <a href="https://colab.research.google.com/drive/1vnxtgPKOgfC8nyCL9hjrNFed75StsqGQ?usp=sharing">
-        <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)
     
@@ -132,22 +66,44 @@ def process_video(video_path):
         yield processed_frame
     cap.release()
 
+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"), 
+    gr.Textbox(label="Solution")
+]
+
+examples = [
+    ["/home/user/app/images/jagung7.jpg", 640, 0.3, 0.6],
+    ["/home/user/app/images/jagung4.jpeg", 640, 0.3, 0.6],
+    ["/home/user/app/images/jagung6.jpeg", 640, 0.3, 0.6]
+]
+
+title = """Corn Diseases Detection Finetuned YOLOv11 <br></br> <a href="https://colab.research.google.com/drive/1vnxtgPKOgfC8nyCL9hjrNFed75StsqGQ?usp=sharing"> <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.'
 
 video_iface = gr.Interface(
     fn=process_video,
-    inputs=[
-        gr.Video(label="Upload Video", interactive=True)
-    ],
-    outputs=gr.Image(type="pil",label="Result"),
+    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")],
-    # ]
+    description="Upload video for inference."
 )
 
-
-image_iface = gr.Interface(fn=response2, inputs=inputs, outputs=outputs, examples=examples, title=title, description=description, theme="dark")
+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"])