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[ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "og8-GROJfqYN" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import 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 + \"`. 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