Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Samples/v_CricketShot_g07_c03.mp4 filter=lfs diff=lfs merge=lfs -text
+Samples/v_PlayingCello_g04_c01.mp4 filter=lfs diff=lfs merge=lfs -text

README (1).md

@@ -0,0 +1,11 @@
+---
+title: Video Classification with CNN-RNN
+emoji: 🎬
+colorFrom: red
+colorTo: yellow
+sdk: gradio
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

README.md

@@ -1,12 +1,6 @@
 ---
 title: Video
-emoji: ⚡
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 5.25.0
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 5.23.3
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Samples-20250408T035849Z-001.zip

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+size 3572793

Samples/v_CricketShot_g07_c03.mp4

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+oid sha256:27bb7648cd61f972a453bf126c48d1796e8af69bb446ceb85f0bfe8d0835a91b
+size 1492498

Samples/v_PlayingCello_g04_c01.mp4

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+size 2079055

app.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 19730,
+     "status": "ok",
+     "timestamp": 1744098140927,
+     "user": {
+      "displayName": "Lan Hoang",
+      "userId": "15367629887304430933"
+     },
+     "user_tz": -420
+    },
+    "id": "Vnowuw1FWR6Y",
+    "outputId": "18eb608c-528b-4a68-84c1-f83e2d1b5091"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "  Resolved https://github.com/tensorflow/docs to commit 18c3a45517af27c8513e165b8a52e6bbc1204b99\n",
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+      "  Preparing metadata (setup.py): finished with status 'done'\n",
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+      "Requirement already satisfied: rpds-py>=0.7.1 in c:\\users\\pc\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from jsonschema>=2.6->nbformat->tensorflow-docs==2025.3.6.10029) (0.20.0)\n",
+      "Requirement already satisfied: platformdirs>=2.5 in c:\\users\\pc\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from jupyter-core!=5.0.*,>=4.12->nbformat->tensorflow-docs==2025.3.6.10029) (4.2.2)\n",
+      "Requirement already satisfied: pywin32>=300 in c:\\users\\pc\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from jupyter-core!=5.0.*,>=4.12->nbformat->tensorflow-docs==2025.3.6.10029) (306)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  Running command git clone --filter=blob:none --quiet https://github.com/tensorflow/docs 'C:\\Users\\PC\\AppData\\Local\\Temp\\pip-req-build-cktlp7ez'\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install gradio\n",
+    "%pip install git+https://github.com/tensorflow/docs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "executionInfo": {
+     "elapsed": 18553,
+     "status": "ok",
+     "timestamp": 1744098159483,
+     "user": {
+      "displayName": "Lan Hoang",
+      "userId": "15367629887304430933"
+     },
+     "user_tz": -420
+    },
+    "id": "nBrcqD-WktG-"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\PC\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "# -*- coding: utf-8 -*-\n",
+    "\"\"\"🎬 Keras Video Classification CNN-RNN model\n",
+    "\n",
+    "Spaces for showing the model usage.\n",
+    "\n",
+    "Author:\n",
+    "    - Thomas Chaigneau @ChainYo\n",
+    "\"\"\"\n",
+    "import os\n",
+    "import cv2\n",
+    "\n",
+    "import gradio as gr\n",
+    "import numpy as np\n",
+    "\n",
+    "from tensorflow import keras\n",
+    "\n",
+    "from tensorflow_docs.vis import embed\n",
+    "\n",
+    "from huggingface_hub import from_pretrained_keras"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "executionInfo": {
+     "elapsed": 109,
+     "status": "ok",
+     "timestamp": 1744098315598,
+     "user": {
+      "displayName": "Lan Hoang",
+      "userId": "15367629887304430933"
+     },
+     "user_tz": -420
+    },
+    "id": "2l8ZXA4-XD_-"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "A subdirectory or file Samples already exists.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%mkdir Samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 677,
+     "referenced_widgets": [
+      "7953decbcc66485d9e477a9d45777f99",
+      "f8709807dedf4e349dd9d463db6abac7",
+      "53a737a6c2794edcbd2d454273753b8b",
+      "5b22eb49678f44c09cf7e5a6007133de",
+      "32364e76989b40b39659c3f93e727fde",
+      "cb23ba857ca548e88967a0ef2d3f32e4",
+      "70331cb723934c37abd7a1815a969841",
+      "7c618cdc320f4cb3909fe11d4124cd85",
+      "d9fb30527f9a4bed9896bf89cbd4eb54",
+      "fd8b65aab251427bac9380d843ad02e1",
+      "9a3b710001334adc8684c2227e2b556b"
+     ]
+    },
+    "executionInfo": {
+     "elapsed": 9546,
+     "status": "ok",
+     "timestamp": 1744103445447,
+     "user": {
+      "displayName": "Lan Hoang",
+      "userId": "15367629887304430933"
+     },
+     "user_tz": -420
+    },
+    "id": "G_1_pfzxlXSo",
+    "outputId": "f1b2fb37-45b1-450b-ff8e-09fcb6f8ee55"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7953decbcc66485d9e477a9d45777f99",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Fetching 11 files:   0%|          | 0/11 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Gradio in a Colab notebook requires sharing enabled. Automatically setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
+      "\n",
+      "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
+      "* Running on public URL: https://06f7718827d3f617a0.gradio.live\n",
+      "\n",
+      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"https://06f7718827d3f617a0.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Kích thước ảnh đầu vào và số lượng đặc trưng\n",
+    "IMG_SIZE = 224\n",
+    "NUM_FEATURES = 2048\n",
+    "\n",
+    "# Tải mô hình CNN-RNN từ HuggingFace\n",
+    "model = from_pretrained_keras(\"keras-io/video-classification-cnn-rnn\")\n",
+    "\n",
+    "# Tạo danh sách video ví dụ từ thư mục Samples\n",
+    "samples = []\n",
+    "for file in os.listdir(\"Samples\"):\n",
+    "    tag = file.split(\"_\")[1]\n",
+    "    samples.append([f\"samples/{file}\"])\n",
+    "\n",
+    "# Cắt phần hình vuông ở trung tâm frame\n",
+    "def crop_center_square(frame):\n",
+    "    y, x = frame.shape[0:2]\n",
+    "    min_dim = min(y, x)\n",
+    "    start_x = (x // 2) - (min_dim // 2)\n",
+    "    start_y = (y // 2) - (min_dim // 2)\n",
+    "    return frame[start_y : start_y + min_dim, start_x : start_x + min_dim]\n",
+    "\n",
+    "# Đọc video và xử lý từng frame\n",
+    "def load_video(path, max_frames=0, resize=(IMG_SIZE, IMG_SIZE)):\n",
+    "    cap = cv2.VideoCapture(path)\n",
+    "    frames = []\n",
+    "    try:\n",
+    "        while True:\n",
+    "            ret, frame = cap.read()\n",
+    "            if not ret:\n",
+    "                break\n",
+    "            frame = crop_center_square(frame)\n",
+    "            frame = cv2.resize(frame, resize)\n",
+    "            frame = frame[:, :, [2, 1, 0]]\n",
+    "            frames.append(frame)\n",
+    "\n",
+    "            if len(frames) == max_frames:\n",
+    "                break\n",
+    "    finally:\n",
+    "        cap.release()\n",
+    "    return np.array(frames)\n",
+    "\n",
+    "# Xây dựng mô hình trích xuất đặc trưng (InceptionV3)\n",
+    "def build_feature_extractor():\n",
+    "    feature_extractor = keras.applications.InceptionV3(\n",
+    "        weights=\"imagenet\",\n",
+    "        include_top=False,\n",
+    "        pooling=\"avg\",\n",
+    "        input_shape=(IMG_SIZE, IMG_SIZE, 3),\n",
+    "    )\n",
+    "    preprocess_input = keras.applications.inception_v3.preprocess_input\n",
+    "\n",
+    "    inputs = keras.Input((IMG_SIZE, IMG_SIZE, 3))\n",
+    "    preprocessed = preprocess_input(inputs)\n",
+    "\n",
+    "    outputs = feature_extractor(preprocessed)\n",
+    "    return keras.Model(inputs, outputs, name=\"feature_extractor\")\n",
+    "\n",
+    "# Tạo feature extractor 1 lần\n",
+    "feature_extractor = build_feature_extractor()\n",
+    "\n",
+    "# Trích xuất đặc trưng cho từng frame của video\n",
+    "def prepare_video(frames, max_seq_length: int = 20):\n",
+    "    frames = frames[None, ...]\n",
+    "    frame_mask = np.zeros(shape=(1, max_seq_length,), dtype=\"bool\")\n",
+    "    frame_features = np.zeros(shape=(1, max_seq_length, NUM_FEATURES), dtype=\"float32\")\n",
+    "\n",
+    "    for i, batch in enumerate(frames):\n",
+    "        video_length = batch.shape[0]\n",
+    "        length = min(max_seq_length, video_length)\n",
+    "        for j in range(length):\n",
+    "            frame_features[i, j, :] = feature_extractor.predict(batch[None, j, :])\n",
+    "        frame_mask[i, :length] = 1  # 1 = not masked, 0 = masked\n",
+    "\n",
+    "    return frame_features, frame_mask\n",
+    "\n",
+    "# Dự đoán hành động từ video\n",
+    "def sequence_prediction(path):\n",
+    "    class_vocab = [\"CricketShot\", \"PlayingCello\", \"Punch\", \"ShavingBeard\", \"TennisSwing\"]\n",
+    "\n",
+    "    frames = load_video(path)\n",
+    "    frame_features, frame_mask = prepare_video(frames)\n",
+    "    probabilities = model.predict([frame_features, frame_mask])[0]\n",
+    "\n",
+    "    preds = {}\n",
+    "    for i in np.argsort(probabilities)[::-1]:\n",
+    "        preds[class_vocab[i]] = float(probabilities[i])\n",
+    "    return preds\n",
+    "\n",
+    "# HTML mô tả bên dưới app\n",
+    "article = article = \"<div style='text-align: center;'><a href='https://github.com/ChainYo' target='_blank'>Space by Thomas Chaigneau</a><br><a href='https://keras.io/examples/vision/video_classification/' target='_blank'>Keras example by Sayak Paul</a></div>\"\n",
+    "\n",
+    "# Tạo giao diện Gradio\n",
+    "app = gr.Interface(\n",
+    "    fn=sequence_prediction,\n",
+    "    inputs=[gr.Video(label=\"Video\")],\n",
+    "    outputs=gr.Label(label=\"Prediction\"),\n",
+    "    title=\"Keras Video Classification with CNN-RNN\",\n",
+    "    description=\"Video classification demo using CNN-RNN based model.\",\n",
+    "    article=article,\n",
+    "    examples=samples\n",
+    ")\n",
+    "\n",
+    "# Khởi chạy ứng dụng\n",
+    "app.launch()\n",
+    "\n"
+   ]
+  }
+ ],
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+  "colab": {
+   "authorship_tag": "ABX9TyNo8ELbXbt4Di76kKEeSLhJ",
+   "provenance": []
+  },
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app.py

@@ -0,0 +1,126 @@
+# -*- coding: utf-8 -*-
+"""🎬 Keras Video Classification CNN-RNN model
+
+Spaces for showing the model usage.
+
+Author:
+    - Thomas Chaigneau @ChainYo
+"""
+import os
+import cv2
+
+import gradio as gr
+import numpy as np
+
+from tensorflow import keras
+
+from tensorflow_docs.vis import embed
+
+from huggingface_hub import from_pretrained_keras
+
+# Kích thước ảnh đầu vào và số lượng đặc trưng
+IMG_SIZE = 224
+NUM_FEATURES = 2048
+
+# Tải mô hình CNN-RNN từ HuggingFace
+model = from_pretrained_keras("keras-io/video-classification-cnn-rnn")
+
+# Tạo danh sách video ví dụ từ thư mục Samples
+samples = []
+for file in os.listdir("Samples"):
+    tag = file.split("_")[1]
+    samples.append([f"samples/{file}"])
+
+# Cắt phần hình vuông ở trung tâm frame
+def crop_center_square(frame):
+    y, x = frame.shape[0:2]
+    min_dim = min(y, x)
+    start_x = (x // 2) - (min_dim // 2)
+    start_y = (y // 2) - (min_dim // 2)
+    return frame[start_y : start_y + min_dim, start_x : start_x + min_dim]
+
+# Đọc video và xử lý từng frame
+def load_video(path, max_frames=0, resize=(IMG_SIZE, IMG_SIZE)):
+    cap = cv2.VideoCapture(path)
+    frames = []
+    try:
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            frame = crop_center_square(frame)
+            frame = cv2.resize(frame, resize)
+            frame = frame[:, :, [2, 1, 0]]
+            frames.append(frame)
+
+            if len(frames) == max_frames:
+                break
+    finally:
+        cap.release()
+    return np.array(frames)
+
+# Xây dựng mô hình trích xuất đặc trưng (InceptionV3)
+def build_feature_extractor():
+    feature_extractor = keras.applications.InceptionV3(
+        weights="imagenet",
+        include_top=False,
+        pooling="avg",
+        input_shape=(IMG_SIZE, IMG_SIZE, 3),
+    )
+    preprocess_input = keras.applications.inception_v3.preprocess_input
+
+    inputs = keras.Input((IMG_SIZE, IMG_SIZE, 3))
+    preprocessed = preprocess_input(inputs)
+
+    outputs = feature_extractor(preprocessed)
+    return keras.Model(inputs, outputs, name="feature_extractor")
+
+# Tạo feature extractor 1 lần
+feature_extractor = build_feature_extractor()
+
+# Trích xuất đặc trưng cho từng frame của video
+def prepare_video(frames, max_seq_length: int = 20):
+    frames = frames[None, ...]
+    frame_mask = np.zeros(shape=(1, max_seq_length,), dtype="bool")
+    frame_features = np.zeros(shape=(1, max_seq_length, NUM_FEATURES), dtype="float32")
+
+    for i, batch in enumerate(frames):
+        video_length = batch.shape[0]
+        length = min(max_seq_length, video_length)
+        for j in range(length):
+            frame_features[i, j, :] = feature_extractor.predict(batch[None, j, :])
+        frame_mask[i, :length] = 1  # 1 = not masked, 0 = masked
+
+    return frame_features, frame_mask
+
+# Dự đoán hành động từ video
+def sequence_prediction(path):
+    class_vocab = ["CricketShot", "PlayingCello", "Punch", "ShavingBeard", "TennisSwing"]
+
+    frames = load_video(path)
+    frame_features, frame_mask = prepare_video(frames)
+    probabilities = model.predict([frame_features, frame_mask])[0]
+
+    preds = {}
+    for i in np.argsort(probabilities)[::-1]:
+        preds[class_vocab[i]] = float(probabilities[i])
+    return preds
+
+# HTML mô tả bên dưới app
+article = article = "<div style='text-align: center;'><a href='https://github.com/ChainYo' target='_blank'>Space by Thomas Chaigneau</a><br><a href='https://keras.io/examples/vision/video_classification/' target='_blank'>Keras example by Sayak Paul</a></div>"
+
+# Tạo giao diện Gradio
+app = gr.Interface(
+    fn=sequence_prediction,
+    inputs=[gr.Video(label="Video")],
+    outputs=gr.Label(label="Prediction"),
+    title="Keras Video Classification with CNN-RNN",
+    description="Video classification demo using CNN-RNN based model.",
+    article=article,
+    examples=samples
+)
+
+# Khởi chạy ứng dụng
+app.launch()
+
+

gitattributes.txt

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

requirements.txt

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+opencv-python-headless
+tensorflow
+git+https://github.com/tensorflow/docs