Upload model

app.py

@@ -1,67 +1,22 @@
 import gradio as gr
-import cv2
 import requests
-import os
 
-from ultralytics import YOLO
+def predict_label(img_path	):
 
-file_urls = [
-    'https://www.dropbox.com/s/b5g97xo901zb3ds/pothole_example.jpg?dl=1',
-    'https://www.dropbox.com/s/86uxlxxlm1iaexa/pothole_screenshot.png?dl=1',
-    'https://www.dropbox.com/s/7sjfwncffg8xej2/video_7.mp4?dl=1'
-]
-
-
-def download_file(url, save_name):
-    url = url
-    if not os.path.exists(save_name):
-        file = requests.get(url)
-        open(save_name, 'wb').write(file.content)
-
-
-for i, url in enumerate(file_urls):
-    if 'mp4' in file_urls[i]:
-        download_file(
-            file_urls[i],
-            f"video.mp4"
-        )
-    else:
-        download_file(
-            file_urls[i],
-            f"image_{i}.jpg"
-        )
-        model = YOLO('best.pt')
-        path = [['image_0.jpg'], ['image_1.jpg']]
-        video_path = [['video.mp4']]
+    results = model(img_path)
+    return map[results[0].probs.data.argmax().item()]
 
-
-        def show_preds_image(image_path):
-            image = cv2.imread(image_path)
-            outputs = model.predict(source=image_path)
-            results = outputs[0].cpu().numpy()
-            for i, det in enumerate(results.boxes.xyxy):
-                cv2.rectangle(
-                    image,
-                    (int(det[0]), int(det[1])),
-                    (int(det[2]), int(det[3])),
-                    color=(0, 0, 255),
-                    thickness=2,
-                    lineType=cv2.LINE_AA
-                )
-            return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
-
-        inputs_image = [
-            gr.components.Image(type="filepath", label="Input Image"),
-        ]
-        outputs_image = [
-            gr.components.Image(type="numpy", label="Output Image"),
-        ]
-        interface_image = gr.Interface(
-            fn=show_preds_image,
-            inputs=inputs_image,
-            outputs=outputs_image,
-            title="Pothole detector",
-            examples=path,
-            cache_examples=False,
-        )
+inputs_image = [
+    gr.components.Image(type="filepath", label="Input Image"),
+]
+outputs_image = [
+    gr.components.Image(type="numpy", label="Output Image"),
+]
+interface_image = gr.Interface(
+    fn=show_preds_image,
+    inputs=inputs_image,
+    outputs=outputs_image,
+    title="Pothole detector",
+    examples=path,
+    cache_examples=False,
+)