Add texts

README.md

@@ -1,8 +1,8 @@
 ---
 title: Orang Outan Image Video Detection
-emoji: 👀
-colorFrom: red
-colorTo: blue
+emoji: 🦧
+colorFrom: green
+colorTo: green
 sdk: gradio
 sdk_version: 4.1.1
 app_file: app.py

app.py

@@ -7,7 +7,22 @@ from PIL import Image
 from ultralytics import YOLO
 
 MARKDOWN = """
-# Orang Outan Detection
+<h1 style="text-align: center;">YOLO Orang Outan Detection 🦧🔍 </h1>
+
+## About the model 👁️
+This is a demo for my YOLOv8 nano trained for orang outan detection.\\
+The model was trained using [this dataset](https://images.cv/dataset/orangutan-image-classification-dataset)
+for orang outan images and [this dataset](https://www.kaggle.com/datasets/slothkong/10-monkey-species/data) as background images. Annotations were obtained using zero shot detection method GroundingDino.\\
+
+The code can be found on my github repository: https://github.com/clementapa/orang-outan-image-video-detection.
+
+## About the orang outans 🦧
+Because to habitat destruction, illicit poaching, and the pet trade, orangutans are in danger of going extinct. Their natural habitat has been significantly reduced by deforestation and the growth of palm oil plantations. Adult orangutans are occasionally sought for their body parts, and they are frequently captured and sold as pets. Climate change and disease are also taking a toll on their populations. Furthermore, it is concerning to note that they are limited to Borneo and Sumatra, two places on Earth. Sustainable practises and conservation initiatives are crucial to preventing the permanent extinction of these amazing animals.
+
+## AI for good 🌍
+Artificial Intelligence (AI) has unquestionable power in the realm of innovation and technology. Even though artificial intelligence (AI) has frequently been used for commercial advantage, it is important to stress that AI can also be used for more noble purposes, such as protecting the environment and the planet's future. We can build a more promising and sustainable future if we reorient AI's focus from business to improving our planet.
+
+
 """
 EXAMPLES = []
 
@@ -49,42 +64,30 @@ def inference(image_rgb_pil: Image.Image, confidence: float) -> List[Image.Image
     )
 
 
-def run_demo():
-    custom_theme = gr.themes.Soft(primary_hue="blue").set(
-        button_secondary_background_fill="*neutral_100",
-        button_secondary_background_fill_hover="*neutral_200",
+custom_theme = gr.themes.Soft(primary_hue="blue").set(
+    button_secondary_background_fill="*neutral_100",
+    button_secondary_background_fill_hover="*neutral_200",
+)
+
+with gr.Blocks(theme=custom_theme, css="style.css") as demo:
+    gr.Markdown(MARKDOWN)
+
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(image_mode="RGB", type="pil", height=500)
+            confidence_slider = gr.Slider(
+                label="Confidence", minimum=0.1, maximum=1.0, step=0.05, value=0.6
+            )
+            submit_button = gr.Button("Let's find orang outans 🦧 !")
+        output_image = gr.Image(label="Results", type="pil")
+
+    submit_button.click(
+        inference,
+        inputs=[input_image, confidence_slider],
+        outputs=output_image,
+        queue=True,
     )
 
-    with gr.Blocks(theme=custom_theme, css="style.css") as demo:
-        gr.Markdown(MARKDOWN)
-
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(image_mode="RGB", type="pil", height=500)
-                confidence_slider = gr.Slider(
-                    label="Confidence", minimum=0.1, maximum=1.0, step=0.05, value=0.6
-                )
-                submit_button = gr.Button("Submit")
-            output_image = gr.Image(label="Results", type="pil")
-
-        # with gr.Row():
-        #     gr.Examples(
-        #         examples=EXAMPLES,
-        #         fn=inference,
-        #         inputs=[input_image, prompt_text, confidence_slider],
-        #         outputs=[gallery],
-        #         cache_examples=True,
-        #         run_on_click=True
-        #     )
-
-        submit_button.click(
-            inference,
-            inputs=[input_image, confidence_slider],
-            outputs=output_image,
-            queue=True,
-        )
-    demo.queue(max_size=20, api_open=False).launch()
-
 
 if __name__ == "__main__":
-    run_demo()
+    demo.queue(max_size=20, api_open=False).launch()

style.css

@@ -3,56 +3,6 @@
     /* Horizontally center the content */
 }
 
-#share-btn-container {
-    padding-left: 0.5rem !important;
-    padding-right: 0.5rem !important;
-    background-color: #000000;
-    justify-content: center;
-    align-items: center;
-    border-radius: 9999px !important;
-    max-width: 13rem;
-    margin-left: auto;
-}
-
-div#share-btn-container>div {
-    flex-direction: row;
-    background: black;
-    align-items: center
-}
-
-#share-btn-container:hover {
-    background-color: #060606
-}
-
-#share-btn {
-    all: initial;
-    color: #ffffff;
-    font-weight: 600;
-    cursor: pointer;
-    font-family: 'IBM Plex Sans', sans-serif;
-    margin-left: 0.5rem !important;
-    padding-top: 0.5rem !important;
-    padding-bottom: 0.5rem !important;
-    right: 0;
-}
-
-#share-btn * {
-    all: unset
-}
-
-#share-btn-container div:nth-child(-n+2) {
-    width: auto !important;
-    min-height: 0px !important;
-}
-
-#share-btn-container .wrap {
-    display: none !important
-}
-
-#share-btn-container.hidden {
-    display: none !important
-}
-
 #duplicate-button {
     margin-left: auto;
     color: #fff;