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"model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "d458dfa418b944d2958d11898f29402d": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "markdown", "source": [ "# 1 – installs" ], "metadata": { "id": "4CafkY908XwI" } }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JNrB6KJM8SoD", "outputId": "6a1a06c6-ac34-4aee-f44a-8096bcc8405a" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 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gradio as gr\n", "from transformers import pipeline\n", "try:\n", " from transformers import Conversation\n", "except ImportError:\n", " # fallback for older Transformers builds\n", " from transformers.pipelines.conversational import Conversation\n", "from speechbrain.pretrained import Tacotron2, HIFIGAN\n", "\n", "import torch, numpy as np\n", "\n", "tacotron2 = Tacotron2.from_hparams(\n", " source=\"speechbrain/tts-tacotron2-ljspeech\"\n", ")\n", "\n", "hifigan = HIFIGAN.from_hparams(\n", " source=\"speechbrain/tts-hifigan-ljspeech\"\n", ")\n", "\n", "# 🔌 Load lightweight models\n", "sentiment_pipe = pipeline(\n", " \"sentiment-analysis\",\n", " model=\"distilbert-base-uncased-finetuned-sst-2-english\"\n", ")\n", "\n", "chat_pipe = pipeline(\n", " \"conversational\",\n", " model=\"microsoft/DialoGPT-medium\",\n", " device_map=\"auto\"\n", ")\n", "\n", "summ_pipe = pipeline(\n", " \"summarization\",\n", " model=\"sshleifer/distilbart-cnn-12-6\",\n", " device_map=\"auto\"\n", ")\n", "\n", "print(\"✅ All models are loaded!\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "c4ba641819db438aa2d789f9b8792580", "6212642a6b6443b5a6f3331cfd424233", "61487ae7059c491aad2315fd8a041367", "370f5c8584ae41c2b15034c38c613257", "7c260146f9194065a4bd40598db945d0", "98adbd90cc3248dbb5334a859a22c886", "03cfd25e9d5e4f81b4878566df275605", "95ad20ea2c6a4b548d6b1701f28f5ca0", "8bb6993f97c04d85ae53aaa862718052", "4189705b42c6434eb24dda9629fa5d53", "5bf9e7120f414aa5939d73efafc0525b", "53522d6c9e1242b09d30936ae0ae9c11", "0fadfa4e5c554d6fb31e7c6cfc714a64", "ccdbc224cce1460c871af0fe4a674b5c", "6fac159b733b411086357e6320d99f8c", "b907d71063484a9396bdd45b126197fc", "d18ef9f969b74a548f40496775929478", "b227114477904d44a1dab5dc3026373e", "95bcd9a7ebcd460fb26e9e56f8d433d7", "d39d583c7ba3456583179e4edf41cc71", "b9efd9a4a5214b84a0eea87451eac2ab", "3ef15d5679504bcb844a0d76bcce9efe", "09c5c965b1eb469ba9aae4e9d9d4611f", 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"c95609363a5343af9d93d4e5f01b5444", "344e1e399ba94771aaf44cc68bace7bb", "fb42038b73394f008014b0368419d1af", "8586663577f44d1dbc58acc2b433c86f", "75c95a92417c41c387894e3a5c3584c1", "87c80ec21d6a45bc9ab4c9cfd5e924ea", "3ae4f799895140b28b685f79fa4c0536", "fdf3756b45b148818d544c8a22916e30", "76bda363f2544c029ccdbe9150647f2e", "05ba6d9eedd949a29146e415d012788c", "f968c3dd70684c62af3044180d6d1f2c", "5e8e3554470448eb9657c4e6014ba2c3", "bc69a4400d794e1ba77393bc5c656bba", "b237bf31f41f41bcb1ba656211a5cd81", "387122ade07b4a1da4ab73b350722c9f", "c88dc96bbf2f4041a5d72603a27fe3e5", "f6a7eb8b3ebc4faa964d504a4a14d2fe", "46592feacf474dc9b603c79d113d70a1", "97efd1112b594d19b6b58c7786b271a9", "e5c2a5db6cda47a3a2321112508d0c6e", "d18e53190f0c42fe9eb45ec5d6099c4d", "f32b407b393b43679752580440777f7b", "7d7ed1afbf7e4c82b1721c977d48da0d", "d458dfa418b944d2958d11898f29402d" ] }, "id": "BumNToOE8rkW", "outputId": "d38ee1c1-2e19-4211-93dc-10d8c9273703" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "DEBUG:speechbrain.utils.checkpoints:Registered checkpoint save hook for _speechbrain_save\n", "DEBUG:speechbrain.utils.checkpoints:Registered checkpoint load hook for _speechbrain_load\n", "DEBUG:speechbrain.utils.checkpoints:Registered checkpoint save hook for save\n", "DEBUG:speechbrain.utils.checkpoints:Registered checkpoint load hook for load\n", "DEBUG:speechbrain.utils.checkpoints:Registered checkpoint save hook for _save\n", "DEBUG:speechbrain.utils.checkpoints:Registered checkpoint load hook for _recover\n", "/tmp/ipython-input-2-1875301684.py:9: UserWarning: Module 'speechbrain.pretrained' was deprecated, redirecting to 'speechbrain.inference'. Please update your script. This is a change from SpeechBrain 1.0. See: https://github.com/speechbrain/speechbrain/releases/tag/v1.0.0\n", " from speechbrain.pretrained import Tacotron2, HIFIGAN\n", "INFO:speechbrain.utils.fetching:Fetch hyperparams.yaml: Fetching from HuggingFace Hub 'speechbrain/tts-tacotron2-ljspeech' if not cached\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "hyperparams.yaml: 0.00B [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "c4ba641819db438aa2d789f9b8792580" } }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:speechbrain.utils.fetching:Fetch custom.py: Fetching from HuggingFace Hub 'speechbrain/tts-tacotron2-ljspeech' if not cached\n", "DEBUG:speechbrain.utils.parameter_transfer:Fetching files for pretraining (no collection directory set)\n", "INFO:speechbrain.utils.fetching:Fetch model.ckpt: Fetching from HuggingFace Hub 'speechbrain/tts-tacotron2-ljspeech' if not cached\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "model.ckpt: 0%| | 0.00/113M [00:00 /root/.cache/huggingface/hub/models--speechbrain--tts-tacotron2-ljspeech/snapshots/d01e530d6d8e1b388c04b882305867addbed4389/model.ckpt\n", "INFO:speechbrain.utils.fetching:Fetch hyperparams.yaml: Fetching from HuggingFace Hub 'speechbrain/tts-hifigan-ljspeech' if not cached\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "hyperparams.yaml: 0.00B [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "09c5c965b1eb469ba9aae4e9d9d4611f" } }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:speechbrain.utils.fetching:Fetch custom.py: Fetching from HuggingFace Hub 'speechbrain/tts-hifigan-ljspeech' if not cached\n", "/usr/local/lib/python3.11/dist-packages/torch/nn/utils/weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`.\n", " WeightNorm.apply(module, name, dim)\n", "DEBUG:speechbrain.utils.parameter_transfer:Fetching files for pretraining (no collection directory set)\n", "INFO:speechbrain.utils.fetching:Fetch generator.ckpt: Fetching from HuggingFace Hub 'speechbrain/tts-hifigan-ljspeech' if not cached\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "generator.ckpt: 0%| | 0.00/55.8M [00:00 /root/.cache/huggingface/hub/models--speechbrain--tts-hifigan-ljspeech/snapshots/17fbdc3aae35b81e1554111fa54eab5f2b70cedb/generator.ckpt\n", "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/629 [00:00" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {}, "execution_count": 3 } ] }, { "cell_type": "markdown", "source": [ "# 3 – Sentiment-Analysis tab" ], "metadata": { "id": "wbqJSD0iA9_Y" } }, { "cell_type": "code", "source": [ "# 💬 Sentiment-Analysis components + callback\n", "def analyze(text):\n", " res = sentiment_pipe(text)[0]\n", " return res[\"label\"], round(res[\"score\"], 4)\n", "\n", "with gr.Blocks() as sentiment_tab:\n", " inp = gr.Textbox(label=\"Enter text\")\n", " btn = gr.Button(\"Analyze\")\n", " label = gr.Textbox(label=\"Sentiment\")\n", " conf = gr.Number(label=\"Confidence\")\n", " btn.click(analyze, inp, [label, conf])\n", "\n", "sentiment_tab\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "v4XxJKKY_Ge2", "outputId": "416b21a8-01cb-4993-bd17-b66d30c691e0" }, "execution_count": 4, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Gradio Blocks instance: 1 backend functions\n", "-------------------------------------------\n", "fn_index=0\n", " inputs:\n", " |-\n", " outputs:\n", " |-\n", " |-" ] }, "metadata": {}, "execution_count": 4 } ] }, { "cell_type": "markdown", "source": [ "# 4 – Summarization tab" ], "metadata": { "id": "y2Gr1CwWBGns" } }, { "cell_type": "code", "source": [ "# 📚 Summarization components + callback\n", "def summarize(text):\n", " return summ_pipe(text,\n", " max_length=130,\n", " min_length=30,\n", " do_sample=False)[0][\"summary_text\"]\n", "\n", "with gr.Blocks() as summarization_tab:\n", " long = gr.Textbox(lines=12, label=\"Paste long text\")\n", " btn = gr.Button(\"Summarize\")\n", " short = gr.Textbox(lines=8, label=\"Summary\")\n", " btn.click(summarize, long, short)\n", "\n", "summarization_tab" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "EIe7Q793BBW_", "outputId": "fb08e04e-7447-4bf1-d784-759499a45fd6" }, "execution_count": 5, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Gradio Blocks instance: 1 backend functions\n", "-------------------------------------------\n", "fn_index=0\n", " inputs:\n", " |-\n", " outputs:\n", " |-" ] }, "metadata": {}, "execution_count": 5 } ] }, { "cell_type": "markdown", "source": [ "# 5 – Text-to-Speech tab" ], "metadata": { "id": "mrp3UJwVBPh-" } }, { "cell_type": "code", "source": [ "# 🔊 Text-to-Speech callback (replace the old one)\n", "def speak(text: str):\n", " text = text.strip()\n", " if not text:\n", " return None # empty input ⇒ no output\n", "\n", " with torch.no_grad():\n", " mel, _, _ = tacotron2.encode_text(text)\n", " wav = hifigan.decode_batch(mel)[0] # shape: [samples]\n", "\n", " # Ensure 1-D float32 numpy\n", " wav = wav.squeeze().cpu().numpy().astype(np.float32)\n", "\n", " return 22050, wav # (sample-rate, waveform)\n", "\n", "with gr.Blocks() as tts_tab:\n", " txt = gr.Textbox(label=\"Text to speak\")\n", " btn = gr.Button(\"Generate voice\")\n", " aud = gr.Audio(label=\"Speech\", type=\"numpy\") # <- keep type=\"numpy\"\n", " btn.click(speak, txt, aud)\n" ], "metadata": { "id": "AFF3Au5WBLZB" }, "execution_count": 6, "outputs": [] }, { "cell_type": "markdown", "source": [ "# 6 – Chatbot tab" ], "metadata": { "id": "1vxohIy-BbGm" } }, { "cell_type": "code", "source": [ "# 6 – Chatbot tab (fixed)\n", "\n", "with gr.Blocks() as chatbot_tab:\n", " state = gr.State([]) # chat history: list[tuple[str, str]]\n", " chatbox = gr.Chatbot()\n", " msg = gr.Textbox(label=\"Your message\", placeholder=\"Type here…\")\n", " send = gr.Button(\"Send\")\n", "\n", " # Step 1: add user bubble instantly & clear input\n", " def add_user(user_text, history):\n", " user_text = user_text.strip()\n", " if not user_text:\n", " return history, history, \"\" # ignore blank\n", " history = history + [(user_text, \"\")] # bot slot empty string\n", " return history, history, \"\" # update chatbox & state, clear msg\n", "\n", " # Step 2: generate bot reply and patch the last tuple\n", " def add_bot(history):\n", " past_users = [u for u, _ in history[:-1]]\n", " past_bots = [b for _, b in history[:-1]]\n", " last_user = history[-1][0]\n", "\n", " conv = Conversation(\n", " text=last_user,\n", " past_user_inputs=past_users,\n", " generated_responses=past_bots\n", " )\n", " chat_pipe(conv)\n", " bot_reply = conv.generated_responses[-1]\n", " history[-1] = (last_user, bot_reply) # fill the placeholder\n", " return history, history # update chatbox & state\n", "\n", " send.click(\n", " add_user,\n", " inputs=[msg, state],\n", " outputs=[chatbox, state, msg], # clear msg input\n", " queue=False\n", " ).then(\n", " add_bot,\n", " inputs=state,\n", " outputs=[chatbox, state], # update with bot reply\n", " queue=True # heavy call can queue\n", " )\n", "\n", " # Enter key behaves the same\n", " msg.submit(\n", " add_user,\n", " inputs=[msg, state],\n", " outputs=[chatbox, state, msg],\n", " queue=False\n", " ).then(\n", " add_bot,\n", " inputs=state,\n", " outputs=[chatbox, state],\n", " queue=True\n", " )\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yw9xryr0BUdv", "outputId": "d7ce1c54-cc1a-4b75-cb88-aba84155c183" }, "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/tmp/ipython-input-11-474994687.py:5: UserWarning: You have not specified a value for the `type` parameter. Defaulting to the 'tuples' format for chatbot messages, but this is deprecated and will be removed in a future version of Gradio. Please set type='messages' instead, which uses openai-style dictionaries with 'role' and 'content' keys.\n", " chatbox = gr.Chatbot()\n" ] } ] }, { "cell_type": "markdown", "source": [ "# 7 – Wrap all tabs & launch" ], "metadata": { "id": "wbZK3jpXBhJl" } }, { "cell_type": "code", "source": [ "# 🏁 Wrap everything in one multi-tab interface\n", "with gr.Blocks(title=\"TrailTrek Gears – AI Demo\") as demo:\n", " with gr.TabItem(\"Sentiment Analysis\"):\n", " sentiment_tab.render()\n", " with gr.TabItem(\"Summarization\"):\n", " summarization_tab.render()\n", " with gr.TabItem(\"Text-to-Speech\"):\n", " tts_tab.render()\n", " with gr.TabItem(\"Chatbot\"):\n", " chatbot_tab.render()\n", "\n", "demo.launch()\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 645 }, "id": "xD6GqYItBfni", "outputId": "a9a114b0-e8b6-4c5f-f2a2-5a97220dcf64" }, "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "It looks like you are running Gradio on a hosted a Jupyter notebook. For the Gradio app to work, sharing must be 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://b688560a30f25232b9.gradio.live\n", "\n", "This share link expires in 1 week. 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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "
" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [] }, "metadata": {}, "execution_count": 12 } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "OctxmjbgBpZM" }, "execution_count": null, "outputs": [] } ] }