Add video tab

app.py

@@ -1,36 +1,92 @@
-import gradio as gr
-from ultralytics import YOLO
-import cv2
-import numpy as np
-
-# Load the YOLOv8 model
-model = YOLO("./model/best.pt")
-
-def detect_emotion(image):
-    """
-    Perform YOLO8 inference on the uploaded image.
-    :param image: Input image from the Gradio interface
-    :return: Annotated image with bounding boxes and emotion labels
-    """
-    # Convert PIL image to OpenCV format
-    image = np.array(image)
-    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-
-    # Perform inference
-    results = model(image)
-
-    # Annotate the image with predictions
-    annotated_image = results[0].plot()
-
-    # Convert OpenCV BGR image back to RGB for display
-    annotated_image = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
-    return annotated_image
-
-# Create Gradio interface
-gr.Interface(
-    fn=detect_emotion,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Image(type="numpy"),
-    title="YOLO8 Object Detection",
-    description="Upload an image, and the model will detect the object with bounding boxes."
-).launch(share=True)  # Added share=True to expose a public link
+import gradio as gr
+from ultralytics import YOLO
+import cv2
+import numpy as np
+
+# Load the YOLOv8 model
+model = YOLO("./model/best.pt")
+
+def detect_emotion(image):
+    """
+    Perform YOLO8 inference on the uploaded image.
+    :param image: Input image from the Gradio interface
+    :return: Annotated image with bounding boxes and emotion labels
+    """
+    # Convert PIL image to OpenCV format
+    image = np.array(image)
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+    # Perform inference
+    results = model(image)
+
+    # Annotate the image with predictions
+    annotated_image = results[0].plot()
+
+    # Convert OpenCV BGR image back to RGB for display
+    annotated_image = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
+    return annotated_image
+
+
+def detect_emotion_video(video_path):
+    """
+    Perform YOLO8 inference on an uploaded video.
+    :param video_path: Path to the video file from Gradio interface
+    :return: Processed video with bounding boxes and emotion labels
+    """
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        return "Error: Could not open video file."
+
+    # Get video properties
+    frame_width = int(cap.get(3))
+    frame_height = int(cap.get(4))
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    
+    # Define the output video writer
+    output_video_path = "output_video.mp4"
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')  
+    out = cv2.VideoWriter(output_video_path, fourcc, fps, (frame_width, frame_height))
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break  # Stop if video ends
+
+        # Perform inference
+        results = model(frame)
+
+        # Annotate the frame with predictions
+        annotated_frame = results[0].plot()
+
+        # Write the processed frame to the output video
+        out.write(annotated_frame)
+
+    cap.release()
+    out.release()
+    
+    return output_video_path  # Return the processed video
+
+
+# Create Gradio Tabs
+with gr.Blocks() as demo:
+    gr.Markdown("## YOLOv8 Emotion Detection")
+
+    with gr.Tabs():
+        # Tab 1: Image Inference
+        with gr.Tab("Image Detection"):
+            gr.Markdown("### Upload an Image for Emotion Detection")
+            image_input = gr.Image(type="pil")
+            image_output = gr.Image(type="numpy")
+            image_btn = gr.Button("Detect Emotion")
+            image_btn.click(detect_emotion, inputs=image_input, outputs=image_output)
+
+        # Tab 2: Video Inference
+        with gr.Tab("Video Detection"):
+            gr.Markdown("### Upload a Video for Emotion Detection")
+            video_input = gr.Video()
+            video_output = gr.Video()
+            video_btn = gr.Button("Detect Emotion in Video")
+            video_btn.click(detect_emotion_video, inputs=video_input, outputs=video_output)
+
+# Launch the Gradio App
+demo.launch(share=True)  # Enables public sharing