Initial file upload

app.py

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+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

requirements.txt

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+gradio
+ultralytics
+cv2
+numpy