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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "header"
},
"source": [
"# 🎬 VideoAI - Free Video Generation\n",
"\n",
"## 100% Free AI Video Generation using Google Colab!\n",
"\n",
"**Features:**\n",
"- ✅ Completely Free\n",
"- ✅ No Credit Card Required\n",
"- ✅ High Quality Videos\n",
"- ✅ Multiple Models\n",
"\n",
"**Instructions:**\n",
"1. Click Runtime → Run all\n",
"2. Wait 2-3 minutes for setup\n",
"3. Enter your prompt in the last cell\n",
"4. Generate and download your video!\n",
"\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "install"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"📦 Installing dependencies...\n",
"zsh:1: no matches found: imageio[ffmpeg]\n",
"✅ Dependencies installed!\n"
]
}
],
"source": [
"# Step 1: Install Dependencies\n",
"print(\"📦 Installing dependencies...\")\n",
"!pip install -q diffusers transformers accelerate imageio[ffmpeg] flask flask-cors pyngrok\n",
"print(\"✅ Dependencies installed!\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "imports"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/sravyalu/VideoAI/.venv/lib/python3.13/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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Libraries imported!\n",
"🔥 GPU Available: False\n"
]
}
],
"source": [
"# Step 2: Import Libraries\n",
"import torch\n",
"from diffusers import CogVideoXPipeline\n",
"from diffusers.utils import export_to_video\n",
"import imageio\n",
"from IPython.display import Video, display\n",
"import os\n",
"\n",
"print(\"✅ Libraries imported!\")\n",
"print(f\"🔥 GPU Available: {torch.cuda.is_available()}\")\n",
"if torch.cuda.is_available():\n",
" print(f\" GPU Name: {torch.cuda.get_device_name(0)}\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "load_model"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🤖 Loading CogVideoX-2B model...\n",
"⏳ This may take 2-3 minutes...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Fetching 14 files: 100%|██████████| 14/14 [02:32<00:00, 10.88s/it]\n",
"Loading pipeline components...: 0%| | 0/5 [00:00<?, ?it/s]`torch_dtype` is deprecated! Use `dtype` instead!\n",
"Loading checkpoint shards: 100%|██████████| 2/2 [00:03<00:00, 1.89s/it]\n",
"Loading pipeline components...: 80%|████████ | 4/5 [00:06<00:01, 1.53s/it]\n"
]
},
{
"ename": "ValueError",
"evalue": "The component <class 'transformers.models.t5.tokenization_t5._LazyModule.__getattr__.<locals>.Placeholder'> of <class 'diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline'> cannot be loaded as it does not seem to have any of the loading methods defined in {'ModelMixin': ['save_pretrained', 'from_pretrained'], 'SchedulerMixin': ['save_pretrained', 'from_pretrained'], 'DiffusionPipeline': ['save_pretrained', 'from_pretrained'], 'OnnxRuntimeModel': ['save_pretrained', 'from_pretrained'], 'PreTrainedTokenizer': ['save_pretrained', 'from_pretrained'], 'PreTrainedTokenizerFast': ['save_pretrained', 'from_pretrained'], 'PreTrainedModel': ['save_pretrained', 'from_pretrained'], 'FeatureExtractionMixin': ['save_pretrained', 'from_pretrained'], 'ProcessorMixin': ['save_pretrained', 'from_pretrained'], 'ImageProcessingMixin': ['save_pretrained', 'from_pretrained'], 'ORTModule': ['save_pretrained', 'from_pretrained']}.",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mValueError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 2\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m🤖 Loading CogVideoX-2B model...\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 3\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m⏳ This may take 2-3 minutes...\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m pipe = \u001b[43mCogVideoXPipeline\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 6\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mTHUDM/CogVideoX-2b\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 7\u001b[39m \u001b[43m \u001b[49m\u001b[43mtorch_dtype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtorch\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfloat16\u001b[49m\n\u001b[32m 8\u001b[39m \u001b[43m)\u001b[49m\n\u001b[32m 10\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m torch.cuda.is_available():\n\u001b[32m 11\u001b[39m pipe.to(\u001b[33m\"\u001b[39m\u001b[33mcuda\u001b[39m\u001b[33m\"\u001b[39m)\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/VideoAI/.venv/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py:114\u001b[39m, in \u001b[36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 111\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[32m 112\u001b[39m kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.\u001b[34m__name__\u001b[39m, has_token=has_token, kwargs=kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m114\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/VideoAI/.venv/lib/python3.13/site-packages/diffusers/pipelines/pipeline_utils.py:1025\u001b[39m, in \u001b[36mDiffusionPipeline.from_pretrained\u001b[39m\u001b[34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[39m\n\u001b[32m 1018\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1019\u001b[39m \u001b[38;5;66;03m# load sub model\u001b[39;00m\n\u001b[32m 1020\u001b[39m sub_model_dtype = (\n\u001b[32m 1021\u001b[39m torch_dtype.get(name, torch_dtype.get(\u001b[33m\"\u001b[39m\u001b[33mdefault\u001b[39m\u001b[33m\"\u001b[39m, torch.float32))\n\u001b[32m 1022\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(torch_dtype, \u001b[38;5;28mdict\u001b[39m)\n\u001b[32m 1023\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m torch_dtype\n\u001b[32m 1024\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m1025\u001b[39m loaded_sub_model = \u001b[43mload_sub_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1026\u001b[39m \u001b[43m \u001b[49m\u001b[43mlibrary_name\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlibrary_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1027\u001b[39m \u001b[43m \u001b[49m\u001b[43mclass_name\u001b[49m\u001b[43m=\u001b[49m\u001b[43mclass_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1028\u001b[39m \u001b[43m \u001b[49m\u001b[43mimportable_classes\u001b[49m\u001b[43m=\u001b[49m\u001b[43mimportable_classes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1029\u001b[39m \u001b[43m \u001b[49m\u001b[43mpipelines\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpipelines\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1030\u001b[39m \u001b[43m \u001b[49m\u001b[43mis_pipeline_module\u001b[49m\u001b[43m=\u001b[49m\u001b[43mis_pipeline_module\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1031\u001b[39m \u001b[43m \u001b[49m\u001b[43mpipeline_class\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpipeline_class\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1032\u001b[39m \u001b[43m \u001b[49m\u001b[43mtorch_dtype\u001b[49m\u001b[43m=\u001b[49m\u001b[43msub_model_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1033\u001b[39m \u001b[43m \u001b[49m\u001b[43mprovider\u001b[49m\u001b[43m=\u001b[49m\u001b[43mprovider\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1034\u001b[39m \u001b[43m \u001b[49m\u001b[43msess_options\u001b[49m\u001b[43m=\u001b[49m\u001b[43msess_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1035\u001b[39m \u001b[43m \u001b[49m\u001b[43mdevice_map\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcurrent_device_map\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1036\u001b[39m \u001b[43m \u001b[49m\u001b[43mmax_memory\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmax_memory\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1037\u001b[39m \u001b[43m \u001b[49m\u001b[43moffload_folder\u001b[49m\u001b[43m=\u001b[49m\u001b[43moffload_folder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1038\u001b[39m \u001b[43m \u001b[49m\u001b[43moffload_state_dict\u001b[49m\u001b[43m=\u001b[49m\u001b[43moffload_state_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1039\u001b[39m \u001b[43m \u001b[49m\u001b[43mmodel_variants\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmodel_variants\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1040\u001b[39m \u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m=\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1041\u001b[39m \u001b[43m \u001b[49m\u001b[43mfrom_flax\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfrom_flax\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1042\u001b[39m \u001b[43m \u001b[49m\u001b[43mvariant\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvariant\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1043\u001b[39m \u001b[43m \u001b[49m\u001b[43mlow_cpu_mem_usage\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlow_cpu_mem_usage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1044\u001b[39m \u001b[43m \u001b[49m\u001b[43mcached_folder\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcached_folder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1045\u001b[39m \u001b[43m \u001b[49m\u001b[43muse_safetensors\u001b[49m\u001b[43m=\u001b[49m\u001b[43muse_safetensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1046\u001b[39m \u001b[43m \u001b[49m\u001b[43mdduf_entries\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdduf_entries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1047\u001b[39m \u001b[43m \u001b[49m\u001b[43mprovider_options\u001b[49m\u001b[43m=\u001b[49m\u001b[43mprovider_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1048\u001b[39m \u001b[43m \u001b[49m\u001b[43mquantization_config\u001b[49m\u001b[43m=\u001b[49m\u001b[43mquantization_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1049\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1050\u001b[39m logger.info(\n\u001b[32m 1051\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mLoaded \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m as \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mclass_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m from `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m` subfolder of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpretrained_model_name_or_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 1052\u001b[39m )\n\u001b[32m 1054\u001b[39m init_kwargs[name] = loaded_sub_model \u001b[38;5;66;03m# UNet(...), # DiffusionSchedule(...)\u001b[39;00m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/VideoAI/.venv/lib/python3.13/site-packages/diffusers/pipelines/pipeline_loading_utils.py:770\u001b[39m, in \u001b[36mload_sub_model\u001b[39m\u001b[34m(library_name, class_name, importable_classes, pipelines, is_pipeline_module, pipeline_class, torch_dtype, provider, sess_options, device_map, max_memory, offload_folder, offload_state_dict, model_variants, name, from_flax, variant, low_cpu_mem_usage, cached_folder, use_safetensors, dduf_entries, provider_options, quantization_config)\u001b[39m\n\u001b[32m 766\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_dummy_path \u001b[38;5;129;01mand\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mdummy\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m none_module:\n\u001b[32m 767\u001b[39m \u001b[38;5;66;03m# call class_obj for nice error message of missing requirements\u001b[39;00m\n\u001b[32m 768\u001b[39m class_obj()\n\u001b[32m--> \u001b[39m\u001b[32m770\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 771\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mThe component \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mclass_obj\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpipeline_class\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m cannot be loaded as it does not seem to have\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 772\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m any of the loading methods defined in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mALL_IMPORTABLE_CLASSES\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 773\u001b[39m )\n\u001b[32m 775\u001b[39m load_method = _get_load_method(class_obj, load_method_name, is_dduf=dduf_entries \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m 777\u001b[39m \u001b[38;5;66;03m# add kwargs to loading method\u001b[39;00m\n",
"\u001b[31mValueError\u001b[39m: The component <class 'transformers.models.t5.tokenization_t5._LazyModule.__getattr__.<locals>.Placeholder'> of <class 'diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline'> cannot be loaded as it does not seem to have any of the loading methods defined in {'ModelMixin': ['save_pretrained', 'from_pretrained'], 'SchedulerMixin': ['save_pretrained', 'from_pretrained'], 'DiffusionPipeline': ['save_pretrained', 'from_pretrained'], 'OnnxRuntimeModel': ['save_pretrained', 'from_pretrained'], 'PreTrainedTokenizer': ['save_pretrained', 'from_pretrained'], 'PreTrainedTokenizerFast': ['save_pretrained', 'from_pretrained'], 'PreTrainedModel': ['save_pretrained', 'from_pretrained'], 'FeatureExtractionMixin': ['save_pretrained', 'from_pretrained'], 'ProcessorMixin': ['save_pretrained', 'from_pretrained'], 'ImageProcessingMixin': ['save_pretrained', 'from_pretrained'], 'ORTModule': ['save_pretrained', 'from_pretrained']}."
]
}
],
"source": [
"# Step 3: Load Model (CogVideoX-2B - Smaller, faster)\n",
"print(\"🤖 Loading CogVideoX-2B model...\")\n",
"print(\"⏳ This may take 2-3 minutes...\")\n",
"\n",
"pipe = CogVideoXPipeline.from_pretrained(\n",
" \"THUDM/CogVideoX-2b\",\n",
" torch_dtype=torch.float16\n",
")\n",
"\n",
"if torch.cuda.is_available():\n",
" pipe.to(\"cuda\")\n",
" print(\"✅ Model loaded on GPU!\")\n",
"else:\n",
" print(\"⚠️ Running on CPU (will be slower)\")\n",
"\n",
"print(\"🎬 Ready to generate videos!\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "generate_function"
},
"outputs": [],
"source": [
"# Step 4: Generate Video Function\n",
"def generate_video(prompt, output_path=\"output.mp4\", num_frames=49):\n",
" \"\"\"\n",
" Generate a video from a text prompt\n",
" \n",
" Args:\n",
" prompt: Text description of the video\n",
" output_path: Where to save the video\n",
" num_frames: Number of frames (default 49 = ~6 seconds)\n",
" \"\"\"\n",
" print(f\"🎨 Generating video for: {prompt}\")\n",
" print(f\"⏳ This will take 30-60 seconds...\")\n",
" \n",
" # Generate\n",
" video_frames = pipe(\n",
" prompt=prompt,\n",
" num_frames=num_frames,\n",
" guidance_scale=6.0,\n",
" num_inference_steps=50\n",
" ).frames[0]\n",
" \n",
" # Save\n",
" export_to_video(video_frames, output_path, fps=8)\n",
" \n",
" print(f\"✅ Video saved to: {output_path}\")\n",
" \n",
" # Display\n",
" display(Video(output_path, embed=True, width=512))\n",
" \n",
" return output_path\n",
"\n",
"print(\"✅ Generation function ready!\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "generate_header"
},
"source": [
"---\n",
"## 🎬 Generate Your Video!\n",
"\n",
"**Edit the prompt below and run the cell:**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "generate"
},
"outputs": [],
"source": [
"# 🎬 EDIT THIS PROMPT:\n",
"prompt = \"A golden retriever running through a field of sunflowers at sunset\"\n",
"\n",
"# Generate!\n",
"video_path = generate_video(prompt)\n",
"\n",
"# Download link\n",
"from google.colab import files\n",
"print(\"\\n📥 Click below to download your video:\")\n",
"files.download(video_path)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "examples"
},
"source": [
"---\n",
"## 💡 Example Prompts\n",
"\n",
"Try these prompts:\n",
"\n",
"```python\n",
"# Nature\n",
"\"A majestic waterfall cascading down rocks in a lush rainforest\"\n",
"\n",
"# Animals\n",
"\"A cat playing with a ball of yarn on a cozy carpet\"\n",
"\n",
"# Urban\n",
"\"City street at night with cars and neon lights\"\n",
"\n",
"# Fantasy\n",
"\"A dragon flying over a medieval castle at dawn\"\n",
"\n",
"# Action\n",
"\"A sports car drifting around a corner on a race track\"\n",
"```\n",
"\n",
"**Just copy a prompt above and paste it in the previous cell!**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "api_header"
},
"source": [
"---\n",
"## 🚀 Optional: Create API Server (Advanced)\n",
"\n",
"Run this to create a temporary API endpoint you can connect to from your app!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "api_server"
},
"outputs": [],
"source": [
"# Optional: Create Flask API + ngrok tunnel\n",
"from flask import Flask, request, jsonify, send_file\n",
"from flask_cors import CORS\n",
"from pyngrok import ngrok\n",
"import threading\n",
"\n",
"app = Flask(__name__)\n",
"CORS(app)\n",
"\n",
"@app.route('/generate-video', methods=['POST'])\n",
"def api_generate():\n",
" data = request.json\n",
" prompt = data.get('prompt', '')\n",
" \n",
" if not prompt:\n",
" return jsonify({'error': 'Prompt required'}), 400\n",
" \n",
" output_path = generate_video(prompt)\n",
" \n",
" return jsonify({\n",
" 'video_url': f'/download/{output_path}',\n",
" 'prompt': prompt\n",
" })\n",
"\n",
"@app.route('/download/<path:filename>')\n",
"def download(filename):\n",
" return send_file(filename, mimetype='video/mp4')\n",
"\n",
"# Start ngrok tunnel\n",
"public_url = ngrok.connect(5000)\n",
"print(f\"\\n🌐 Your API is live at: {public_url}\")\n",
"print(f\"\\n📝 Use this URL in your frontend!\")\n",
"print(f\" Replace http://localhost:5000 with: {public_url}\")\n",
"\n",
"# Run Flask\n",
"threading.Thread(target=lambda: app.run(port=5000, debug=False)).start()"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"provenance": []
},
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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