add examples

app.py

@@ -21,6 +21,30 @@ examples = [
         "movies/bathroom.mp4",
         ROOMS,
     ],
+    [
+        "movies/bedroom.mp4",
+        ROOMS,
+    ],
+    [
+        "movies/dressing.mp4",
+        ROOMS,
+    ],
+    [
+        "movies/home-office.mp4",
+        ROOMS,
+    ],
+    [
+        "movies/kitchen.mp4",
+        ROOMS,
+    ],
+    [
+        "movies/living-room.mp4",
+        ROOMS,
+    ],
+    [
+        "movies/toilet.mp4",
+        ROOMS,
+    ],
 ]
 
 
@@ -45,28 +69,12 @@ def get_num_total_frames(file_path: str):
     return len(videoreader)
 
 
-# def convert_frames_to_gif(frames, save_path: str = "frames.gif"):
-#     converted_frames = frames.astype(np.uint8)
-#     Path(save_path).parent.mkdir(parents=True, exist_ok=True)
-#     imageio.mimsave(save_path, converted_frames, fps=8)
-#     return save_path
-
-
-# def create_gif_from_video_file(
-#     file_path: str,
-#     num_frames: int = 16,
-#     frame_sampling_rate: int = 1,
-#     save_path: str = "frames.gif",
-# ):
-#     frames = sample_frames_from_video_file(file_path, num_frames, frame_sampling_rate)
-#     return convert_frames_to_gif(frames, save_path)
-
-
 def select_model(model_name):
     global processor, model
     processor = AutoProcessor.from_pretrained(model_name)
     model = AutoModel.from_pretrained(model_name)
 
+
 def get_frame_sampling_rate(video_path, num_model_input_frames):
     # rearrange sampling rate based on video length and model input length
     num_total_frames = get_num_total_frames(video_path)
@@ -76,6 +84,7 @@ def get_frame_sampling_rate(video_path, num_model_input_frames):
         frame_sampling_rate = FRAME_SAMPLING_RATE
     return frame_sampling_rate
 
+
 def predict(video_path, labels_text):
     labels = labels_text.split(",")
     num_model_input_frames = model.config.vision_config.num_frames
@@ -83,8 +92,6 @@ def predict(video_path, labels_text):
     frames = sample_frames_from_video_file(
         video_path, num_model_input_frames, frame_sampling_rate
     )
-    # gif_path = convert_frames_to_gif(frames, save_path="video.gif")
-
     inputs = processor(
         text=labels, videos=list(frames), return_tensors="pt", padding=True
     )
@@ -103,49 +110,33 @@ def predict(video_path, labels_text):
 
 app = gr.Blocks()
 with app:
+    gr.Markdown("# **<p align='center'>Classification of Rooms</p>**")
     gr.Markdown(
-        "# **<p align='center'>Classification of Rooms</p>**"
-    )
-    gr.Markdown(
-        "### **<p align='center'>Upload a video of a room and provide a list of type of rooms the model should select from.</p>**"
-
+        "#### **<p align='center'>Upload a video (mp4) of a room and provide a list of type of rooms the model should select from.</p>**"
     )
 
     with gr.Row():
         with gr.Column():
             video_file = gr.Video(label="Video File:", show_label=True)
-            local_video_labels_text = gr.Textbox(
-                label="Labels Text:", show_label=True
-            )
+            local_video_labels_text = gr.Textbox(label="Labels Text:", show_label=True)
             submit_button = gr.Button(value="Predict")
-        # with gr.Column():
-        #     video_gif = gr.Image(
-        #         label="Input Clip",
-        #         show_label=True,
-        #     )
         with gr.Column():
             predictions = gr.Label(label="Predictions:", show_label=True)
 
     gr.Markdown("**Examples:**")
-    # gr.Examples(
-    #     examples,
-    #     [video_file,local_video_labels_text],
-    #     [predictions, video_gif],
-    #     fn=predict,
-    #     cache_examples=True,
-    # )
-    
+    gr.Examples(
+        examples,
+        [video_file, local_video_labels_text],
+        predictions,
+        fn=predict,
+        cache_examples=True,
+    )
+
     submit_button.click(
         predict,
         inputs=[video_file, local_video_labels_text],
-        # outputs=[predictions, video_gif],
         outputs=predictions,
     )
-    # gr.Markdown(
-    #     """
-    #     \n Created by: Vincent Claes, <a href=\"https://www.meet-drift.ai/\">Drift</a>.
-    #     \n Inspired by: <a href=\"https://huggingface.co/spaces/fcakyon/zero-shot-video-classification\">fcakyon</a>.
-    #     """
-    # )
+
 
 app.launch()