Add new file

.gitignore

@@ -0,0 +1,7 @@
+flagged/
+*.pt
+*.png
+*.jpg
+*.mp4
+*.mkv
+gradio_cached_examples/

app.py

@@ -0,0 +1,95 @@
+import gradio as gr
+import cv2
+import os
+from ultralyticsplus import YOLO, render_result
+
+model_path= 'best.pt'
+
+def preds_image(image, conf_thres, iou_thres):
+
+    model = YOLO(model_path)
+
+    result = model.predict(image, 
+                           conf= conf_thres,
+                           iou= iou_thres)
+
+    box = result[0].boxes
+    print("Object type: ", box.cls)
+    print("Coordinates: ", box.xyxy)
+    print("Probability: ", box.conf)
+
+    render = render_result(model=model, image=image, result=result[0])
+    return render
+ 
+inputs_image = [
+    gr.Image(label="Input Image"),
+    gr.Slider(minimum=0.0, maximum=1.0, value=0.40, step=0.05, label="Confidence threshold"),
+    gr.Slider(minimum=0.0, maximum=1.0, value=0.45, step=0.05, label="IOU threshold")
+]
+outputs_image = [
+    gr.Image(label="Output Image"),
+]
+
+interface_image = gr.Interface(
+    fn=preds_image,
+    inputs=inputs_image,
+    outputs=outputs_image,
+    title="Chili Leaf Disease Detector (Image)"
+)
+
+def preds_video(video):
+
+    video_path = video
+    video_path_out = '{}_out.mp4'.format(video_path)
+    
+    cap = cv2.VideoCapture(video_path)
+    ret, frame = cap.read()
+    H, W, _ = frame.shape
+    out = cv2.VideoWriter(video_path_out, cv2.VideoWriter_fourcc(*'MP4V'), int(cap.get(cv2.CAP_PROP_FPS)), (W, H))
+
+    model= YOLO(model_path)
+
+    threshold = 0.4
+
+    while ret:
+
+        results = model(frame)[0]
+
+        for result in results.boxes.data.tolist():
+            x1, y1, x2, y2, score, class_id = result
+
+            if score > threshold:
+                cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 0, 255), 4)
+            
+                #object details
+                org = (int(x1), int(y1 - 10))
+                font = cv2.FONT_HERSHEY_SIMPLEX
+                fontscale = 0.5
+                color = (0, 0, 0)
+            
+                cv2.putText(frame, results.names[int(class_id)].lower(), org,
+                            font, fontscale, color, 1, cv2.LINE_AA)
+
+        out.write(frame)
+        ret, frame = cap.read()
+
+    cap.release()
+    out.release()
+    cv2.destroyAllWindows()
+
+    return video_path_out
+    
+inputs_video = gr.Video(label= 'Original chili leaf video')
+outputs_video = gr.Video(label= 'Predicted leaf')
+
+interface_video = gr.Interface(
+    fn=preds_video,
+    inputs=inputs_video,
+    outputs=outputs_video,
+    title="Chili Leaf Disease Detector (Video)"
+)
+
+gr.TabbedInterface(
+    [interface_image, interface_video],
+    tab_names=['Image inference', 'Video inference']
+).queue().launch()

requirements.txt

@@ -0,0 +1,5 @@
+gradio==4.22.0
+opencv_python==4.7.0.72
+opencv_python==4.9.0.80
+opencv_python_headless==4.8.0.74
+ultralyticsplus==0.1.0