add update app.py

.gradio/certificate.pem

@@ -0,0 +1,31 @@
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+-----END CERTIFICATE-----

app.py

@@ -1,43 +1,145 @@
-from ultralytics import YOLO
-from PIL import Image
 import gradio as gr
 from huggingface_hub import snapshot_download
+from ultralytics import YOLO
 import os
+from PIL import Image
+import cv2
+import tempfile
 
-#public model path location
+#Public model path location
 #MODEL_REPO_ID = "mintheinwin/3907578Y"
 
-#Organizations model path location
+#Organization's(NYP) model path location
 MODEL_REPO_ID = "ITI107-2024S2/3907578Y"
 
-#load model
+# Load model
 def load_model(repo_id):
     download_dir = snapshot_download(repo_id)
-    print(download_dir)
-    path  = os.path.join(download_dir, "best_int8_openvino_model")
-    print(path)
-    detection_model = YOLO(path, task='detect')
+    path = os.path.join(download_dir, "best_int8_openvino_model")
+    detection_model = YOLO(path, task="detect")
     return detection_model
-    
+
+# Initialize the model
 detection_model = load_model(MODEL_REPO_ID)
 
-#Student ID
+# Student info
 student_info = "Student Id: 3907578Y, Name: Min Thein Win"
 
-#prdeict 
-def predict(pilimg):
-    source = pilimg
-    result = detection_model.predict(source, conf=0.5, iou=0.5)
-    img_bgr = result[0].plot()
-    out_pilimg = Image.fromarray(img_bgr[..., ::-1])  # RGB-order PIL image
+# Prediction function for images
+def predict_image(pil_img):
+    result = detection_model.predict(pil_img, conf=0.5, iou=0.5)
+    img_bgr = result[0].plot()  # Annotated image
+    out_pilimg = Image.fromarray(img_bgr[..., ::-1])  # Convert to RGB PIL image
     return out_pilimg
-    
-#UI interface
-gr.Markdown("# Wild Animal Detection (Tiger/Lion)")
-gr.Markdown(student_info)
-gr.Interface(fn=predict,
-             inputs=gr.Image(type="pil",label="Input"),
-             outputs=gr.Image(type="pil",label="Output"),
-             title="Wild Animal Detection (Tiger/Lion)",
-             description=student_info,
-             ).launch(share=True)
+
+# Prediction function for videos
+def predict_video(video):
+    cap = cv2.VideoCapture(video)
+    temp_dir = tempfile.mkdtemp()
+    output_path = os.path.join(temp_dir, "annotated_video.mp4")
+
+    frames = []
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # Perform detection on each frame
+        result = detection_model.predict(frame, conf=0.5, iou=0.5)
+        annotated_frame = result[0].plot()
+        frames.append(annotated_frame)
+
+    cap.release()
+
+    # Save annotated video
+    if frames:
+        height, width, _ = frames[0].shape
+        out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), 20, (width, height))
+        for frame in frames:
+            out.write(frame)
+        out.release()
+
+    return output_path
+
+# UI Interface
+def create_interface():
+    with gr.Blocks() as interface:
+        # Header
+        gr.Markdown("# Wild Animal Detection (Lion/Tiger)")
+        gr.Markdown(student_info)
+
+        with gr.Row(equal_height=True): # Input and output view are aligned vertically
+            # Input Section
+            with gr.Column(scale=1): 
+                gr.Markdown("### Detection Input Image/Video:") 
+                input_type = gr.Radio(
+                    choices=["Image", "Video"],
+                    value="Image",  # Default selection is "Image"
+                    label="Select Input Type",
+                    interactive=True,
+                )
+                input_image = gr.Image(type="pil", label="Upload Image", visible=True)  # Default is visible
+                input_video = gr.Video(label="Upload Video", visible=False)  # Default is hidden
+
+                def toggle_inputs(input_choice):
+                    if input_choice == "Image":
+                        return gr.update(visible=True), gr.update(visible=False)
+                    else:
+                        return gr.update(visible=False), gr.update(visible=True)
+
+                input_type.change(toggle_inputs, inputs=input_type, outputs=[input_image, input_video])
+
+            # Output Section
+            with gr.Column(scale=1):  # Adjust scale for proportional width
+                gr.Markdown("### Detection Output Image/Video:")
+                gr.Markdown(
+                     "<p style=color: gray;'>"
+                    "This section displays detected objects with bounding boxes, animal types (Lion/Tiger), and confidence levels.</p>")
+                output_image = gr.Image(type="pil", label="Detection Result (Image)", visible=True)  # Default is visible
+                output_video = gr.Video(label="Detection Result (Video)", visible=False)  # Default is hidden
+
+        #Detect Function
+        def detect(input_choice, image=None, video=None):
+            if input_choice == "Image" and image is not None:
+                return predict_image(image), None
+            elif input_choice == "Video" and video is not None:
+                return None, predict_video(video)
+            return None, None
+
+        # Buttons in Horizontal Layout
+        with gr.Row():
+            detect_btn = gr.Button("START DETECT", variant="primary")
+            reset_btn = gr.Button("RESET", variant="secondary")
+
+        detect_btn.click(
+            fn=detect,
+            inputs=[input_type, input_image, input_video],
+            outputs=[output_image, output_video],
+        )
+
+        # Reset Function
+        def reset_view():
+            return None, None
+
+        reset_btn.click(fn=reset_view, inputs=None, outputs=[output_image, output_video])
+
+        # Dynamic Output Visibility
+        def update_output_visibility(input_choice):
+            if input_choice == "Image":
+                return gr.update(visible=True), gr.update(visible=False)
+            else:
+                return gr.update(visible=False), gr.update(visible=True)
+
+        input_type.change(update_output_visibility, inputs=input_type, outputs=[output_image, output_video])
+       
+        #Footer view
+        gr.Markdown("---")
+        gr.Markdown(
+            "<p style='text-align: center;color: gray;'>"
+            "Developed by NYP student @ Min Thein Win</p>"
+        )
+    return interface
+
+# Launch App
+interface = create_interface()
+interface.launch(share=True)