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matplotlib.pyplot as plt\n", "import seaborn as sns\n" ] }, { "cell_type": "code", "source": [], "metadata": { "id": "uKwqiispiUXG" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ed2c69cd", "outputId": "96b7f49c-0602-42ea-e5c5-115c3ced5b5a" }, "source": [ "pip install transformers and torch" ], "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: transformers in /usr/local/lib/python3.12/dist-packages (4.57.1)\n", "\u001b[31mERROR: Could not find a version that satisfies the requirement and (from versions: none)\u001b[0m\u001b[31m\n", "\u001b[0m\u001b[31mERROR: No matching distribution found for and\u001b[0m\u001b[31m\n", "\u001b[0m" ] } ] }, { "cell_type": "code", "source": [ "import os\n", "import torch\n", "import gradio as gr\n", "from transformers import AutoTokenizer, AutoModelForCausalLM\n", "\n", "# -------- Model loading --------\n", "MODEL_NAME = os.getenv(\"MODEL_NAME\", \"gpt2\") # you can set to gpt2-medium via Space secrets/variables\n", "\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n", "# GPT-2 has no pad token by default; set it to EOS to avoid warnings\n", "if tokenizer.pad_token is None:\n", " tokenizer.pad_token = tokenizer.eos_token\n", "\n", "model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)\n", "model.to(device)\n", "model.eval()\n", "\n", "# -------- Generation helper --------\n", "TONE_PRESETS = {\n", " \"Neutral\": \"neutral, informative, and clear\",\n", " \"Professional\": \"formal, concise, and authoritative\",\n", " \"Friendly\": \"warm, engaging, and helpful\",\n", " \"Persuasive\": \"confident, compelling, and benefit-focused\",\n", " \"Storytelling\": \"narrative, vivid, and descriptive\",\n", "}\n", "\n", "LENGTH_TO_TOKENS = {\n", " \"Short (~300-500 words)\": 380,\n", " \"Medium (~600-900 words)\": 720,\n", " \"Long (~1000-1400 words)\": 1080,\n", "}\n", "\n", "\n", "def build_prompt(title: str, outline: str, tone_label: str) -> str:\n", " tone = TONE_PRESETS.get(tone_label, TONE_PRESETS[\"Neutral\"])\n", " outline_block = f\"\\nOutline:\\n{outline.strip()}\\n\" if outline and outline.strip() else \"\\n\"\n", "\n", " prompt = (\n", " f\"You are a senior content writer. Write a high-quality blog article.\\n\"\n", " f\"Title: {title.strip()}\\n\"\n", " f\"Tone: {tone}\\n\"\n", " f\"Audience: general readers with interest in the topic.\\n\"\n", " f\"Structure: catchy introduction, well-organized sections with subheadings, bullet points where helpful, and a concluding takeaway.\\n\"\n", " f\"Use simple language, avoid fluff, and keep paragraphs short.\\n\"\n", " f\"Add a short, compelling meta description at the end.\\n\"\n", " f\"{outline_block}\\n\"\n", " f\"Begin the article below:\\n\"\n", " )\n", " return prompt\n", "\n", "\n", "def generate_blog(title, outline, tone, length_choice, temperature, top_p, top_k, repetition_penalty, seed):\n", " if not title or not title.strip():\n", " return \"Please provide a blog title/topic.\"\n", "\n", " if seed is not None and seed != \"\":\n", " try:\n", " seed_int = int(seed)\n", " torch.manual_seed(seed_int)\n", " if torch.cuda.is_available():\n", " torch.cuda.manual_seed_all(seed_int)\n", " except ValueError:\n", " pass\n", "\n", " prompt = build_prompt(title, outline, tone)\n", "\n", " inputs = tokenizer(prompt, return_tensors=\"pt\")\n", " input_len = inputs[\"input_ids\"].shape[-1]\n", " inputs = {k: v.to(device) for k, v in inputs.items()}\n", "\n", " max_new_tokens = LENGTH_TO_TOKENS.get(length_choice, 720)\n", "\n", " with torch.inference_mode():\n", " output_ids = model.generate(\n", " **inputs,\n", " max_new_tokens=max_new_tokens,\n", " do_sample=True,\n", " temperature=float(temperature),\n", " top_p=float(top_p),\n", " top_k=int(top_k),\n", " repetition_penalty=float(repetition_penalty),\n", " pad_token_id=tokenizer.eos_token_id,\n", " eos_token_id=tokenizer.eos_token_id,\n", " )\n", "\n", " generated = tokenizer.decode(output_ids[0][input_len:], skip_special_tokens=True)\n", "\n", " # Light post-process: ensure it starts clean and trim over-long endings\n", " text = generated.strip()\n", "\n", " # Safety note\n", " disclaimer = (\n", " \"\\n\\n\\n—\\nNote: This draft is AI-generated using a pretrained GPT‑2 model. \"\n", " \"Facts may be inaccurate. Please review and edit before publishing.\"\n", " )\n", " return text + disclaimer\n", "\n", "\n", "# -------- Gradio UI --------\n", "with gr.Blocks(title=\"Blog Generator • GPT‑2\") as demo:\n", " gr.Markdown(\n", " \"# ✍️ Blog Generator (GPT‑2)\\n\"\n", " \"Generate blog drafts with a pretrained GPT‑2 model. \"\n", " \"For best results, write a clear title and (optional) outline.\"\n", " )\n", "\n", " with gr.Row():\n", " title = gr.Textbox(label=\"Blog Title / Topic\", placeholder=\"e.g., How to Start a Budget in 2025\", lines=1)\n", "\n", " outline = gr.Textbox(\n", " label=\"Optional Outline or Notes\",\n", " placeholder=\"Bullet points, key sections, facts to include…\",\n", " lines=8,\n", " )\n", "\n", " with gr.Row():\n", " tone = gr.Dropdown(\n", " choices=list(TONE_PRESETS.keys()),\n", " value=\"Neutral\",\n", " label=\"Tone\",\n", " )\n", " length_choice = gr.Radio(\n", " choices=list(LENGTH_TO_TOKENS.keys()),\n", " value=\"Medium (~600-900 words)\",\n", " label=\"Length\",\n", " )\n", "\n", " with gr.Accordion(\"Advanced Settings\", open=False):\n", " with gr.Row():\n", " temperature = gr.Slider(0.1, 1.5, value=0.9, step=0.05, label=\"Temperature\")\n", " top_p = gr.Slider(0.1, 1.0, value=0.95, step=0.01, label=\"Top-p\")\n", " top_k = gr.Slider(0, 200, value=50, step=1, label=\"Top-k\")\n", " with gr.Row():\n", " repetition_penalty = gr.Slider(1.0, 2.0, value=1.12, step=0.01, label=\"Repetition Penalty\")\n", " seed = gr.Textbox(label=\"Seed (optional, for reproducibility)\", placeholder=\"e.g., 42\")\n", "\n", " generate_btn = gr.Button(\"Generate Article\", variant=\"primary\")\n", " output = gr.Textbox(label=\"Draft Article\", lines=28)\n", "\n", " generate_btn.click(\n", " fn=generate_blog,\n", " inputs=[title, outline, tone, length_choice, temperature, top_p, top_k, repetition_penalty, seed],\n", " outputs=output,\n", " api_name=\"generate\",\n", " )\n", "\n", " gr.Markdown(\n", " \"**Tips**: If the text loops or gets off-topic, lower temperature or top‑p, increase repetition penalty, or add a more detailed outline.\\n\"\n", " \"**Model**: Using `\" + MODEL_NAME + \"`. You can set a different model name via the `MODEL_NAME` environment variable in your Space Settings.\"\n", " )\n", "\n", "if __name__ == \"__main__\":\n", " demo.queue().launch()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 870, "referenced_widgets": [ "54570210306c408f8eb1e1c5788771d8", "8866c39bf894401bba21d19377a834fa", "544938309cf74c15942857462dd04a0c", "1be1a4bcae4e432aa308e0a007fce2d0", "8be112997b8b47a58fa04c8e523dbd27", "277c6e67c7ca41508565eedb3b225fe8", "28f9d4adf887473eae4682edc66a4590", "fc9f8cd39a7a4559b6f865e92b6193e9", "5f8cf4627d0a4f7aacc05f733c1000eb", "e54427eb125c4ac28348d1ba974e765f", "8daf8f294c6d4559a9649ce77f68f3c1", "817203b9ae89405cb7552946c3dc0c76", "20fc438c74bf4262823bd2297d5da0ab", "1dc91d79543b47eebf4e61f853099a04", "b948b988529f43bc87409d54e32c4e09", "963be29a14e642e6b1071f7cc272311e", "6aca34967a3c4fa5a7a73a8230fd418f", "d4f9b040288e47f2beea26a83395a284", "8a56da7b5b8a46198171995afe012a46", "dd519ee4c3f541d9ac3914f9e882b2a2", "2635ae8dcbdc4d499b5ac44dbd1a3364", "85f13b99f8f94b319cb6a0de077ca00d", "59d58ec69f534525b9b32bae9d928188", "3a6ecc40fb7d415abcc56586102cf96e", "c0622450ab3f4382be81505d458c1f64", "b1d42878d00045b6a8a6d058ca886db1", "04d59112a49140b7874d8201d85de6f0", "5ec883f4952b4715a57f69a468b3d07c", "6ed525ff38784c6998baa53045cc09d4", "c18666ed2b1645bbb30722716e35c925", "b5c010d889564c53a5fc392cbbd40fcf", "a4c3125a686c4fd294601b9e79a6fd58", "99977888a85246e1ba9bbb0338729906", "7736522a216e4e278169ac0d949a86af", "bf42af246c534598922f699a58f23ccd", "480a3850a6d943ca960846846e5a231f", "91c14ac8ff9e41938ffe5efb707865de", "1cfe2481eb5742fe8f727719c0e27f3a", "47053f7706164e0094c9a0671b9db1f3", "f95eb5a9014a4e2bb34e74e619a6f8f1", "f40176de403f450897ddf03856647915", "691a1926d18c4b6eb439ef6a078c6d3b", "d8f83acbaf0942898c233f9d8813c173", "b561c8e199ba4d49b308738ce123d0af", "55e33fb1b11540ca94b0b99d213aded5", "38d7fda99261410bb775730bf0bc5e1c", "32f035bd6f2d4b74b1f84144fbcd49fc", "8f989532b08945d3af55bc2734fef437", "8b9f027720bd4fc2b5a41df8e6055ccf", "23afcd3450664400895f459e1f2a990c", "cce05cd916584b1eb185b02d59392e02", "513033f321a0476085ffba2c761f2f72", "d3ce1300081d4ea5bbb804057162545a", "f0b45c1fcfc84a79a7c867930e926886", "803f94b7aff84140b38782a4bb19fdd9", "131c0863cc4e4ad8b4c459ef3b609720", "2db15243dff249b58a386a90fad2a6c5", "57a32c150c514b2c853fb329c6c82daf", "3e0f3a722c6949a0b90ecfcb098cba67", "d37e4b882a2f49e9a5c708e778b78118", "8a09b3c0432d41cfa302ba93ada0928d", "30f1e471465841e487d9f392b9ab4638", "a49569a880a740bebf2196f9402c67a8", "b9e6001a46ac4e379bed19ccc897a86a", "53ce1dd0469149b39abd90fea714bbae", "6834da697247447185335f6ccf2f3a3b", "5c2f6265e5bb45d9a04ae7611374f511", "d8bda6d84a6841178105f45bb96121ec", "0ad0d4b850874c418a59b6f97c399387", "90aa93fad2fd4d09a5d4547c3b8d614b", "d52bae68612d49a1aad0393a5beebc21", "2ceaf426ba9749c99bbc2bf02548cff8", "cbe5c8e2aada4b86923d99761cd1a2d5", "baca095b529c446fb3405cf019a97808", "53a201bfe0484fa4b564f219fdfb9d08", "a79425483543455f8abd9ae8addb40e1", "3acf1691938646889ded4fcf1d6df90c" ] }, "id": "2k4OdqeSjcdQ", "outputId": "52d9d12c-3404-4bcc-88c0-247fa85f69e3" }, "execution_count": 3, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/26.0 [00:00" ], "text/html": [ "
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