app.py update

app.py

@@ -33,284 +33,346 @@ from plasticorigins.tracking.track_video import track_video
 from plasticorigins.tracking.trackers import get_tracker
 
 
-logger = logging.getLogger()
-logger.setLevel(logging.DEBUG)
-ch = logging.StreamHandler()
-ch.setLevel(logging.DEBUG)
-formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
-ch.setFormatter(formatter)
-logger.addHandler(ch)
-
-class DotDict(dict):
-    """dot.notation access to dictionary attributes"""
-    __getattr__ = dict.get
-    __setattr__ = dict.__setitem__
-    __delattr__ = dict.__delitem__
-
-id_categories = {
-    0: 'Fragment',    #'Sheet / tarp / plastic bag / fragment',
-    1: 'Insulating',  #'Insulating material',
-    2: 'Bottle',      #'Bottle-shaped',
-    3: 'Can',         #'Can-shaped',
-    4: 'Drum',
-    5: 'Packaging',   #'Other packaging',
-    6: 'Tire',
-    7: 'Fishing net', #'Fishing net / cord',
-    8: 'Easily namable',
-    9: 'Unclear'
-}
-
-
-config_track = DotDict({
-    "yolo_conf_thrld": 0.35,
-    "yolo_iou_thrld": 0.5,
-
-    "confidence_threshold": 0.004, # for the tracking part
-    "detection_threshold": 0.3, # for centernet
-    "downsampling_factor": 4,
-    "noise_covariances_path": "data/tracking_parameters",
-    "output_shape": (960,544),
-    "size": 768,
-    "skip_frames": 3, #3
-    "arch": "mobilenet_v3_small",
-    "device": "cpu",
-    "detection_batch_size": 1,
-    "display": 0,
-    "kappa": 4, #4
-    "tau": 3, #4
-    "max_length": 240,
-    "downscale_output":2
-})
-
-
-logger.info('---Yolo model...')
-# Yolo has warning problems, so we set an env variable to remove it
-os.environ["VERBOSE"] = "False"
-URL_MODEL = "https://github.com/surfriderfoundationeurope/IA_Pau/releases/download/v0.1/yolov5.pt"
-FILE_MODEL = "yolov5.pt"
-model_path = download_from_url(URL_MODEL, FILE_MODEL, "./models", logger)
-model_yolo = load_model(model_path, config_track.device, config_track.yolo_conf_thrld, config_track.yolo_iou_thrld)
-
-
-logger.info('---Centernet model...')
-URL_MODEL = "https://partage.imt.fr/index.php/s/sJi22N6gedN6T4q/download"
-FILE_MODEL = "mobilenet_v3_pretrained.pth"
-model_path = download_from_url(URL_MODEL, FILE_MODEL, "./models", logger)
-model = get_mobilenet_v3_small(num_layers=0, heads={'hm': 1}, head_conv=256)
-checkpoint = torch.load(model_path, map_location="cpu")
-model.load_state_dict(checkpoint['model'], strict=True)
-
-
-URL_DEMO1 = "https://etlplasticostorageacc.blob.core.windows.net/surfnetbenchmark/video_niv15.mp4"
-FILE_DEMO1 = "video_niv15.mp4"
-download_from_url(URL_DEMO1, FILE_DEMO1, "./data/", logger)
-video1_path = op.join("./data", FILE_DEMO1)
-URL_DEMO2 = "https://etlplasticostorageacc.blob.core.windows.net/surfnetbenchmark/video_midouze15.mp4"
-FILE_DEMO2 = "video_midouze15.mp4"
-download_from_url(URL_DEMO2, FILE_DEMO2, "./data/", logger)
-video2_path = op.join("./data", FILE_DEMO2)
-URL_DEMO3 = "https://etlplasticostorageacc.blob.core.windows.net/surfnetbenchmark/video_antoine15.mp4"
-FILE_DEMO3 = "video_antoine15.mp4"
-download_from_url(URL_DEMO3, FILE_DEMO3, "./data/", logger)
-video3_path = op.join("./data", FILE_DEMO3)
-JSON_FILE_PATH = "data/"
-
-
-labels2icons = load_trash_icons("./data/icons/")
-
-def track(args):
-    device = torch.device("cpu")
-
-    engine = get_tracker('EKF')
-
-    detector = None
-    # centernet version
-    if args.model_type == "yolo":
-        logger.info("---Using Yolo")
-        detector = lambda frame: predict_yolo(model_yolo, frame, size=config_track.size, augment=False)
-    elif args.model_type == "centernet":
-        logger.info("---Using Centernet")
-        detector = lambda frame: detect(frame, threshold=args.detection_threshold, model=model)
-
-
-    transition_variance = np.load(op.join(args.noise_covariances_path, 'transition_variance.npy'))
-    observation_variance = np.load(op.join(args.noise_covariances_path, 'observation_variance.npy'))
-
-    logger.info(f'---Processing {args.video_path}')
-    reader = IterableFrameReader(video_filename=args.video_path,
-                                 skip_frames=args.skip_frames,
-                                 output_shape=args.output_shape,
-                                 progress_bar=True,
-                                 preload=False,
-                                 max_frame=args.max_length)
-
-
-    input_shape = reader.input_shape
-    output_shape = reader.output_shape
-    ratio_y = input_shape[0] / (output_shape[0] // args.downsampling_factor)
-    ratio_x = input_shape[1] / (output_shape[1] // args.downsampling_factor)
-
-    detections = []
-    logger.info('---Detecting...')
-    if args.model_type == "yolo":
-        with warnings.catch_warnings():
-            warnings.filterwarnings("ignore")
-
-            for frame in reader:
-                detections.append(detector(frame))
-    elif args.model_type == "centernet":
-        detections = get_detections_for_video(reader, detector, batch_size=args.detection_batch_size, device=device)
-
-    logger.info('---Tracking...')
-    display = None
-    results = track_video(reader, iter(detections), args, engine, transition_variance, observation_variance, display, is_yolo=args.model_type=="yolo")
-    reader.video.release()
-
-    # store unfiltered results
-    datestr = datetime.datetime.now().strftime('%Y%m%d%H%M%S%f')
-    output_filename = op.splitext(args.video_path)[0] + "_" + datestr + '_unfiltered.txt'
-    write_tracking_results_to_file(results, ratio_x=ratio_x, ratio_y=ratio_y, output_filename=output_filename)
-    logger.info('---Filtering...')
-
-    # read from the file
-    results = read_tracking_results(output_filename)
-    filtered_results = filter_tracks(results, config_track.kappa, config_track.tau)
-    # store filtered results
-    output_filename = op.splitext(args.video_path)[0] + "_" + datestr + '_filtered.txt'
-    write_tracking_results_to_file(filtered_results, ratio_x=ratio_x, ratio_y=ratio_y, output_filename=output_filename)
-
-    return filtered_results
-
-
-def run_model(video_path, model_type, seconds, skip, tau, kappa, gps_file):
-    logger.info('---video filename: '+ video_path)
-
-    # launch the tracking
-    config_track.video_path = video_path
-    config_track.model_type = model_type
-    config_track.skip_frames = int(skip)
-    config_track.tau = int(tau)
-    config_track.kappa = int(kappa)
-    config_track.max_length = int(seconds)*24
-
-    out_folder = create_unique_folder("/tmp/", "output")
-    output_path = op.join(out_folder, "video.mp4")
-    filtered_results = track(config_track)
-
-    # postprocess
-    logger.info('---Postprocessing...')
-    output_json_path = op.join(out_folder, "output.json")
-    output_json = postprocess_for_api(filtered_results, id_categories)
-    with open(output_json_path, 'w') as f_out:
-        json.dump(output_json, f_out)
-
-    # build video output
-    logger.info('---Generating new video...')
-    reader = IterableFrameReader(video_filename=config_track.video_path,
-                                 skip_frames=0,
-                                 progress_bar=True,
-                                 preload=False,
-                                 max_frame=config_track.max_length)
-
-    # Get GPS Data
-    video_duration = reader.total_num_frames / reader.fps
-    gps_data = get_filled_gps(gps_file, video_duration)
-
-    # Generate new video
-    generate_video_with_annotations(reader, output_json, output_path,
-                                    config_track.skip_frames, config_track.max_length,
-                                    config_track.downscale_output, logger, gps_data=gps_data,
-                                    labels2icons=labels2icons)
-    output_label = count_objects(output_json, id_categories)
-
-
-    # Get Plastic Map
-    map_frame = None # default value in case no GPS file
-    if gps_data is not None:
-        logger.info('---Creating Plastic Map...')
-        # Get Trash Prediction
-        with open(output_json_path) as json_file:
-            predictions = json.load(json_file)
-            trash_df = get_df_prediction(predictions, reader.fps)
-        if len(trash_df) != 0 :
-            # Get Trash prediction alongside GPS data
-            trash_gps_df = get_trash_gps_df(trash_df,gps_data)
-            trash_gps_geo_df = get_trash_gps_geo_df(trash_gps_df)
-            # Create Map
-            center_lat = trash_gps_df.iloc[0]['Latitude']
-            center_long = trash_gps_df.iloc[0]['Longitude']
-            map_path = get_plastic_map(center_lat,center_long,trash_gps_geo_df,out_folder)
-            html_content = codecs.open(map_path, 'r')
-            map_html = html_content.read()
-            map_frame =  f"""<iframe style="width: 100%; height: 480px" name="result" allow="midi; geolocation; microphone; camera;
-        display-capture; encrypted-media;" sandbox="allow-modals allow-forms
-        allow-scripts allow-same-origin allow-popups
-        allow-top-navigation-by-user-activation allow-downloads" allowfullscreen=""
-        allowpaymentrequest="" frameborder="0" srcdoc='{map_html}'></iframe>"""
-
-    logger.info('---Surfnet End processing...')
-
-    return output_path, map_frame, output_label, output_json_path
-
-
-
-def get_filled_gps(file_obj, video_duration)->list:
-    """Get a filled GPS point list from Plastic Origin mobile GPS JSON track
-    Args:
-        file_obj: a file_obj from gradio input File type
-        video_duration: in seconds
-    Returns:
-        gps_data (list): the GPS filled data as a list
-    """
-
-    if file_obj is not None:
-        json_data = parse_json(file_obj)
-        json_data_list = get_json_gps_list(json_data)
-        gps_data = fill_gps(json_data_list, video_duration)
-        return gps_data
-    else:
-        return None
-
-
-def get_plastic_map(center_lat,center_long,trash_gps_gdf,out_folder)->str:
-    """Get the map with plastic trash detection
-    Args:
-        center_lat (float): latitude to center map
-        center_long (float): longitude to center map
-        trash_gps_gdf (DataFrame): trash & gps geo dataframe
-        out_folder (str): folder to save html map
-    Returns:
-        map_html_path (str): full path to html map
-    """
-
-    m = folium.Map([center_lat, center_long], zoom_start=16)
-    locs = zip(trash_gps_gdf.geometry.y,trash_gps_gdf.geometry.x)
-    labels = list(trash_gps_gdf['label'])
-    i = 0
-    for location in locs:
-        folium.CircleMarker(location=location).add_child(folium.Popup(labels[i])).add_to(m)
-        i = i + 1
-        map_html_path = op.join(out_folder,"plasticmap.html")
-    m.save(map_html_path)
-    return map_html_path
-
-
-
-video_in = gr.inputs.Video(type="mp4", source="upload", label="Video Upload", optional=False)
-model_type = gr.inputs.Dropdown(choices=["centernet", "yolo"], type="value", default="yolo", label="model")
-skip_slider = gr.inputs.Slider(minimum=0, maximum=15, step=1, default=3, label="skip frames")
-tau_slider = gr.inputs.Slider(minimum=1, maximum=7, step=1, default=3, label="tau")
-kappa_slider = gr.inputs.Slider(minimum=1, maximum=7, step=1, default=4, label="kappa")
-seconds_num = gr.inputs.Number(default=10, label="seconds")
-gps_in = gr.inputs.File(type="file", label="GPS Upload", optional=True)
-
-
-gr.Interface(fn=run_model, inputs=[video_in, model_type, seconds_num, skip_slider, tau_slider, kappa_slider,gps_in],
-             outputs=["playable_video","html","label", "file"],
-             title="Surfnet demo",
-             examples=[[video1_path, "yolo", 10, 3, 3, 4, JSON_FILE_PATH+"gavepau.json"],
-                       [video2_path, "yolo", 10, 3, 3, 4, JSON_FILE_PATH+"midouze.json"],
-                       [video3_path, "yolo", 10, 3, 3, 4, JSON_FILE_PATH+"gavepau.json"]],
-             description="Upload a video, optionnaly a GPS file and you'll get Plastic detection on river.",
-             theme="huggingface",
-             allow_screenshot=False, allow_flagging="never").launch(debug=True,enable_queue=True)
+
+
+demo = gr.Blocks()
+title = "Surfnet AI Demo"
+with demo:
+    with gr.Tabs():
+        with gr.TabItem("The Project"):
+            gr.HTML(""" <!DOCTYPE html>
+<html lang="en-us">
+  <head>
+    <meta charset="utf-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no">
+    <title>Surfnet AI Demo</title>
+  </head>
+
+  <body>
+    <h1 style="text-align: center;"> <b>Welcome to the Surfnet demo, the algo that tracks Plastic Pollution</b> 🌊</h1>
+            <p style="text-align: center;"> We all dream about swimming in clear blue waters and walking
+              bare-footed on a beautiful white sand beach. But our dream is
+              threatened. Plastics are invading every corner of the earth, 
+              from remote alpine lakes to the deepest oceanic trench.
+               Thankfully, there are many things we can do.🤝
+              <b>Plastic Origins</b>, a citizen science project from <a href="https://surfrider.eu" target = "_blank"><b><u>Surfrider Europe</u></b></a>, 
+              using artificial intelligence to map river plastic pollution, is one of them.
+               This demo is here for you to test the AI model we use to detect and count litter items on riverbanks.
+            </p> 
+            <br>
+                <p style="text-align: center;">
+      ❓ Read more on <a href="https://plasticorigins.eu" target = "_blank"> <b><u>www.plasticorigins.eu</u></b></a>
+         <br>
+         💻 Join the dev team on <a href="https://github.com/surfriderfoundationeurope/The-Plastic-Origins-Project" target = "_blank"> <b><u>Github</u></b></a>
+         <br>
+         🏷️ Help us label images on <a href="https://www.trashroulette.com/#/" target = "_blank"> <b><u>www.trashroulette.com</u></b></a> 
+          <br>
+          <br>
+             <p style="text-align: center">
+      📧 contact :
+        <br>
+        <a href="mailto:[email protected]"> <b><u>[email protected]</u></b></a>
+    </p>
+          </div>
+  </body>
+</html>""")
+        with gr.TabItem("Surfnet AI"):
+            gr.HTML(""" <!DOCTYPE html>
+<html lang="en"> 
+<head>
+<title>Left Side Panel</title>
+<meta charset="utf-8">
+</head>
+
+<body> 
+   <p style="text-align: center;">
+        <b>Surfnet</b> is an AI model that detects trash on riverbanks.
+         We use it to map river plastic pollution and act to reduce the introduction of litter into the environment.
+         Developed & Maintain by a bunch of amazing volunteers from the NGO Surfrider Foundation Europe.
+    </p>
+</body>
+</html>""" )
+
+
+            logger = logging.getLogger()
+            logger.setLevel(logging.DEBUG)
+            ch = logging.StreamHandler()
+            ch.setLevel(logging.DEBUG)
+            formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+            ch.setFormatter(formatter)
+            logger.addHandler(ch)
+
+            class DotDict(dict):
+                """dot.notation access to dictionary attributes"""
+                __getattr__ = dict.get
+                __setattr__ = dict.__setitem__
+                __delattr__ = dict.__delitem__
+
+            id_categories = {
+                0: 'Fragment',    #'Sheet / tarp / plastic bag / fragment',
+                1: 'Insulating',  #'Insulating material',
+                2: 'Bottle',      #'Bottle-shaped',
+                3: 'Can',         #'Can-shaped',
+                4: 'Drum',
+                5: 'Packaging',   #'Other packaging',
+                6: 'Tire',
+                7: 'Fishing net', #'Fishing net / cord',
+                8: 'Easily namable',
+                9: 'Unclear'
+            }
+
+
+            config_track = DotDict({
+                "yolo_conf_thrld": 0.35,
+                "yolo_iou_thrld": 0.5,
+
+                "confidence_threshold": 0.004, # for the tracking part
+                "detection_threshold": 0.3, # for centernet
+                "downsampling_factor": 4,
+                "noise_covariances_path": "data/tracking_parameters",
+                "output_shape": (960,544),
+                "size": 768,
+                "skip_frames": 3, #3
+                "arch": "mobilenet_v3_small",
+                "device": "cpu",
+                "detection_batch_size": 1,
+                "display": 0,
+                "kappa": 4, #4
+                "tau": 3, #4
+                "max_length": 240,
+                "downscale_output":2
+            })
+
+
+            logger.info('---Yolo model...')
+            # Yolo has warning problems, so we set an env variable to remove it
+            os.environ["VERBOSE"] = "False"
+            URL_MODEL = "https://github.com/surfriderfoundationeurope/IA_Pau/releases/download/v0.1/yolov5.pt"
+            FILE_MODEL = "yolov5.pt"
+            model_path = download_from_url(URL_MODEL, FILE_MODEL, "./models", logger)
+            model_yolo = load_model(model_path, config_track.device, config_track.yolo_conf_thrld, config_track.yolo_iou_thrld)
+
+
+            logger.info('---Centernet model...')
+            URL_MODEL = "https://partage.imt.fr/index.php/s/sJi22N6gedN6T4q/download"
+            FILE_MODEL = "mobilenet_v3_pretrained.pth"
+            model_path = download_from_url(URL_MODEL, FILE_MODEL, "./models", logger)
+            model = get_mobilenet_v3_small(num_layers=0, heads={'hm': 1}, head_conv=256)
+            checkpoint = torch.load(model_path, map_location="cpu")
+            model.load_state_dict(checkpoint['model'], strict=True)
+
+
+            URL_DEMO1 = "https://etlplasticostorageacc.blob.core.windows.net/surfnetbenchmark/video_niv15.mp4"
+            FILE_DEMO1 = "video_niv15.mp4"
+            download_from_url(URL_DEMO1, FILE_DEMO1, "./data/", logger)
+            video1_path = op.join("./data", FILE_DEMO1)
+            URL_DEMO2 = "https://etlplasticostorageacc.blob.core.windows.net/surfnetbenchmark/video_midouze15.mp4"
+            FILE_DEMO2 = "video_midouze15.mp4"
+            download_from_url(URL_DEMO2, FILE_DEMO2, "./data/", logger)
+            video2_path = op.join("./data", FILE_DEMO2)
+            URL_DEMO3 = "https://etlplasticostorageacc.blob.core.windows.net/surfnetbenchmark/video_antoine15.mp4"
+            FILE_DEMO3 = "video_antoine15.mp4"
+            download_from_url(URL_DEMO3, FILE_DEMO3, "./data/", logger)
+            video3_path = op.join("./data", FILE_DEMO3)
+            JSON_FILE_PATH = "data/"
+
+
+            labels2icons = load_trash_icons("./data/icons/")
+
+            def track(args):
+                device = torch.device("cpu")
+
+                engine = get_tracker('EKF')
+
+                detector = None
+                # centernet version
+                if args.model_type == "yolo":
+                    logger.info("---Using Yolo")
+                    detector = lambda frame: predict_yolo(model_yolo, frame, size=config_track.size, augment=False)
+                elif args.model_type == "centernet":
+                    logger.info("---Using Centernet")
+                    detector = lambda frame: detect(frame, threshold=args.detection_threshold, model=model)
+
+
+                transition_variance = np.load(op.join(args.noise_covariances_path, 'transition_variance.npy'))
+                observation_variance = np.load(op.join(args.noise_covariances_path, 'observation_variance.npy'))
+
+                logger.info(f'---Processing {args.video_path}')
+                reader = IterableFrameReader(video_filename=args.video_path,
+                                            skip_frames=args.skip_frames,
+                                            output_shape=args.output_shape,
+                                            progress_bar=True,
+                                            preload=False,
+                                            max_frame=args.max_length)
+
+
+                input_shape = reader.input_shape
+                output_shape = reader.output_shape
+                ratio_y = input_shape[0] / (output_shape[0] // args.downsampling_factor)
+                ratio_x = input_shape[1] / (output_shape[1] // args.downsampling_factor)
+
+                detections = []
+                logger.info('---Detecting...')
+                if args.model_type == "yolo":
+                    with warnings.catch_warnings():
+                        warnings.filterwarnings("ignore")
+
+                        for frame in reader:
+                            detections.append(detector(frame))
+                elif args.model_type == "centernet":
+                    detections = get_detections_for_video(reader, detector, batch_size=args.detection_batch_size, device=device)
+
+                logger.info('---Tracking...')
+                display = None
+                results = track_video(reader, iter(detections), args, engine, transition_variance, observation_variance, display, is_yolo=args.model_type=="yolo")
+                reader.video.release()
+
+                # store unfiltered results
+                datestr = datetime.datetime.now().strftime('%Y%m%d%H%M%S%f')
+                output_filename = op.splitext(args.video_path)[0] + "_" + datestr + '_unfiltered.txt'
+                write_tracking_results_to_file(results, ratio_x=ratio_x, ratio_y=ratio_y, output_filename=output_filename)
+                logger.info('---Filtering...')
+
+                # read from the file
+                results = read_tracking_results(output_filename)
+                filtered_results = filter_tracks(results, config_track.kappa, config_track.tau)
+                # store filtered results
+                output_filename = op.splitext(args.video_path)[0] + "_" + datestr + '_filtered.txt'
+                write_tracking_results_to_file(filtered_results, ratio_x=ratio_x, ratio_y=ratio_y, output_filename=output_filename)
+
+                return filtered_results
+
+
+            def run_model(video_path, model_type, seconds, skip, tau, kappa, gps_file):
+                logger.info('---video filename: '+ video_path)
+
+                # launch the tracking
+                config_track.video_path = video_path
+                config_track.model_type = model_type
+                config_track.skip_frames = int(skip)
+                config_track.tau = int(tau)
+                config_track.kappa = int(kappa)
+                config_track.max_length = int(seconds)*24
+
+                out_folder = create_unique_folder("/tmp/", "output")
+                output_path = op.join(out_folder, "video.mp4")
+                filtered_results = track(config_track)
+
+                # postprocess
+                logger.info('---Postprocessing...')
+                output_json_path = op.join(out_folder, "output.json")
+                output_json = postprocess_for_api(filtered_results, id_categories)
+                with open(output_json_path, 'w') as f_out:
+                    json.dump(output_json, f_out)
+
+                # build video output
+                logger.info('---Generating new video...')
+                reader = IterableFrameReader(video_filename=config_track.video_path,
+                                            skip_frames=0,
+                                            progress_bar=True,
+                                            preload=False,
+                                            max_frame=config_track.max_length)
+
+                # Get GPS Data
+                video_duration = reader.total_num_frames / reader.fps
+                gps_data = get_filled_gps(gps_file, video_duration)
+
+                # Generate new video
+                generate_video_with_annotations(reader, output_json, output_path,
+                                                config_track.skip_frames, config_track.max_length,
+                                                config_track.downscale_output, logger, gps_data=gps_data,
+                                                labels2icons=labels2icons)
+                output_label = count_objects(output_json, id_categories)
+
+
+                # Get Plastic Map
+                map_frame = None # default value in case no GPS file
+                if gps_data is not None:
+                    logger.info('---Creating Plastic Map...')
+                    # Get Trash Prediction
+                    with open(output_json_path) as json_file:
+                        predictions = json.load(json_file)
+                        trash_df = get_df_prediction(predictions, reader.fps)
+                    if len(trash_df) != 0 :
+                        # Get Trash prediction alongside GPS data
+                        trash_gps_df = get_trash_gps_df(trash_df,gps_data)
+                        trash_gps_geo_df = get_trash_gps_geo_df(trash_gps_df)
+                        # Create Map
+                        center_lat = trash_gps_df.iloc[0]['Latitude']
+                        center_long = trash_gps_df.iloc[0]['Longitude']
+                        map_path = get_plastic_map(center_lat,center_long,trash_gps_geo_df,out_folder)
+                        html_content = codecs.open(map_path, 'r')
+                        map_html = html_content.read()
+                        map_frame =  f"""<iframe style="width: 100%; height: 480px" name="result" allow="midi; geolocation; microphone; camera;
+                    display-capture; encrypted-media;" sandbox="allow-modals allow-forms
+                    allow-scripts allow-same-origin allow-popups
+                    allow-top-navigation-by-user-activation allow-downloads" allowfullscreen=""
+                    allowpaymentrequest="" frameborder="0" srcdoc='{map_html}'></iframe>"""
+
+                logger.info('---Surfnet End processing...')
+
+                return output_path, map_frame, output_label, output_json_path
+
+
+
+            def get_filled_gps(file_obj, video_duration)->list:
+                """Get a filled GPS point list from Plastic Origin mobile GPS JSON track
+                Args:
+                    file_obj: a file_obj from gradio input File type
+                    video_duration: in seconds
+                Returns:
+                    gps_data (list): the GPS filled data as a list
+                """
+
+                if file_obj is not None:
+                    json_data = parse_json(file_obj)
+                    json_data_list = get_json_gps_list(json_data)
+                    gps_data = fill_gps(json_data_list, video_duration)
+                    return gps_data
+                else:
+                    return None
+
+
+            def get_plastic_map(center_lat,center_long,trash_gps_gdf,out_folder)->str:
+                """Get the map with plastic trash detection
+                Args:
+                    center_lat (float): latitude to center map
+                    center_long (float): longitude to center map
+                    trash_gps_gdf (DataFrame): trash & gps geo dataframe
+                    out_folder (str): folder to save html map
+                Returns:
+                    map_html_path (str): full path to html map
+                """
+
+                m = folium.Map([center_lat, center_long], zoom_start=16)
+                locs = zip(trash_gps_gdf.geometry.y,trash_gps_gdf.geometry.x)
+                labels = list(trash_gps_gdf['label'])
+                i = 0
+                for location in locs:
+                    folium.CircleMarker(location=location).add_child(folium.Popup(labels[i])).add_to(m)
+                    i = i + 1
+                    map_html_path = op.join(out_folder,"plasticmap.html")
+                m.save(map_html_path)
+                return map_html_path
+
+
+
+            video_in = gr.inputs.Video(type="mp4", source="upload", label="Video Upload", optional=False)
+            model_type = gr.inputs.Dropdown(choices=["centernet", "yolo"], type="value", default="yolo", label="model")
+            skip_slider = gr.inputs.Slider(minimum=0, maximum=15, step=1, default=3, label="skip frames")
+            tau_slider = gr.inputs.Slider(minimum=1, maximum=7, step=1, default=3, label="tau")
+            kappa_slider = gr.inputs.Slider(minimum=1, maximum=7, step=1, default=4, label="kappa")
+            seconds_num = gr.inputs.Number(default=10, label="seconds")
+            gps_in = gr.inputs.File(type="file", label="GPS Upload", optional=True)
+
+
+            gr.Interface(fn=run_model, inputs=[video_in, model_type, seconds_num, skip_slider, tau_slider, kappa_slider,gps_in],
+                        outputs=["playable_video","html","label", "file"],
+                        examples=[[video1_path, "yolo", 10, 3, 3, 4, JSON_FILE_PATH+"gavepau.json"],
+                                  [video2_path, "yolo", 10, 3, 3, 4, JSON_FILE_PATH+"midouze.json"],
+                                  [video3_path, "yolo", 10, 3, 3, 4, JSON_FILE_PATH+"gavepau.json"]],
+                        description="Upload a video, optionnaly a GPS file and you'll get Plastic detection on river.",
+                        theme="huggingface",
+                        allow_screenshot=False, allow_flagging="never")
+
+demo.launch()