diff --git a/week3/community-contributions/Week3_Exercise_Synthetic_Dataset_Generator.ipynb b/week3/community-contributions/Week3_Exercise_Synthetic_Dataset_Generator.ipynb new file mode 100644 index 0000000..1767bcb --- /dev/null +++ b/week3/community-contributions/Week3_Exercise_Synthetic_Dataset_Generator.ipynb @@ -0,0 +1,5035 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "OvBwRxvXhzpF" + }, + "source": [ + "# Synthetic Dataset Generator" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-2MSGQC8uwuA" + }, + "source": [ + "## 0. Setup and sign in to Hugging Face" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 37639, + "status": "ok", + "timestamp": 1759633893217, + "user": { + "displayName": "thinsri@gmx.de", + "userId": "18422164867366802681" + }, + "user_tz": -120 + }, + "id": "ciBOYWb4uq9h", + "outputId": "5b4a31a9-ea21-4e91-ebee-01a0e3c77030" + }, + "outputs": [], + "source": [ + "!pip install -q requests bitsandbytes==0.46.0 transformers==4.48.3 accelerate==1.3.0 openai" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yDnr7d2607E5" + }, + "outputs": [], + "source": [ + "import os\n", + "import requests\n", + "import io\n", + "import tempfile\n", + "import torch\n", + "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, TextStreamer, TextIteratorStreamer\n", + "from google.colab import userdata\n", + "from huggingface_hub import login\n", + "from IPython.display import display, Markdown, update_display\n", + "from threading import Thread\n", + "from dotenv import load_dotenv\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "tZz5QoVJ1GTL" + }, + "outputs": [], + "source": [ + "hf_token = userdata.get('HF_TOKEN')\n", + "login(hf_token, add_to_git_credential=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "96RmuUrnut9G" + }, + "source": [ + "## 1. Code Prototyping" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5T_fMfFguzDM" + }, + "outputs": [], + "source": [ + "# Define the model name\n", + "LLAMA = \"meta-llama/Meta-Llama-3.1-8B-Instruct\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "voGGr8V21P6-" + }, + "outputs": [], + "source": [ + "def load_model(model_name):\n", + " quant_config = BitsAndBytesConfig(\n", + " load_in_4bit=True,\n", + " bnb_4bit_use_double_quant=True,\n", + " bnb_4bit_quant_type=\"nf4\",\n", + " bnb_4bit_compute_dtype=torch.bfloat16\n", + " )\n", + " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", + " tokenizer.pad_token = tokenizer.eos_token\n", + " model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=quant_config, device_map=\"auto\")\n", + " return tokenizer, model\n", + "\n", + "def generate_stream_with_thread(messages, tokenizer, model):\n", + " inputs = tokenizer.apply_chat_template(messages, return_tensors=\"pt\", return_attention_mask=True).to(\"cuda\")\n", + " input_token_len = inputs[0].shape[-1] # Get the length of the input tokens\n", + " streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n", + "\n", + " # Generate in separate thread\n", + " thread = Thread(target=model.generate, kwargs={\"inputs\": inputs, \"max_new_tokens\": 500, \"streamer\": streamer})\n", + " thread.start()\n", + "\n", + " # Stream and optionally filter output\n", + " unwanted_patterns = [\"assistant\", \"<|\", '|>']\n", + " response = \"\"\n", + " for text in streamer:\n", + " if text.strip() in unwanted_patterns:\n", + " continue\n", + " else:\n", + " print(text, end=\"\")\n", + " thread.join()\n", + "\n", + "def generate_stream_with_thread_gradio(messages, tokenizer, model):\n", + " \"\"\"Same as generate_stream_with_thread but yield accumulated reply\"\"\"\n", + " inputs = tokenizer.apply_chat_template(messages, return_tensors=\"pt\", return_attention_mask=True).to(\"cuda\")\n", + " input_token_len = inputs[0].shape[-1] # Get the length of the input tokens\n", + " streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n", + "\n", + " # Generate in separate thread\n", + " thread = Thread(target=model.generate, kwargs={\"inputs\": inputs, \"max_new_tokens\": 500, \"streamer\": streamer})\n", + " thread.start()\n", + "\n", + " # Stream and optionally filter output\n", + " unwanted_patterns = [\"assistant\", \"<|\", '|>']\n", + " response = \"\"\n", + " for text in streamer:\n", + " if text.strip() in unwanted_patterns:\n", + " continue\n", + " else:\n", + " response += text\n", + " yield response\n", + " thread.join()\n", + "\n", + "def generate_answer(messages, tokenizer, model):\n", + " inputs = tokenizer.apply_chat_template(messages, return_tensors=\"pt\", return_attention_mask=True).to(\"cuda\")\n", + " input_token_len = inputs[0].shape[-1] # Get the length of the input tokens\n", + " outputs = model.generate(inputs, max_new_tokens=500)\n", + " decoded_output = tokenizer.decode(outputs[0][input_token_len:], skip_special_tokens=True, clean_up_tokenization_spaces=True)\n", + " return decoded_output.replace(\"assistant\\n\\n\", \"\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 401, + "referenced_widgets": [ + "744ef0af763041b69cc272bd549280a2", + "fea2a012f93d4830992a69d7f970f738", + "75ebc5599c744a0a86ab66fcb7bc5639", + "9080517ad5b5457e83425628cc1f7391", + "bbb47fb60e554cabbdcfd524e6558818", + "ea47a5153ad445f3a28820d6c77fefff", + "77914334d6114da2af45add566b6f5c5", + "af70eeabfa784e1f8b51be4feb13fad2", + "0640dbea14a745fca2f6fa5f962ac775", + "c8c2f465ffe14afc99a2ab529189208a", + "bcf0d83c6828416681d05d97d342ba7f", + "ada15f657b5549f3a0ebd42c87edae53", + "e2bc87a955534be495f05e9bc1b45029", + "cda6d00d5daf4f06a58f36655b431293", + "b663362b43ed4e47b5daad7414b2db06", + "03020848cfc34cdea1c65de137c624a7", + "1be17ce8b78141c687c21ce194bcc763", + "deacaa0a0a314886bce184524764215d", + "7494d5c09ddb4259873b6e463adedbaa", + "b61e3d318e4b44b0890ac56e787892c5", + "936883d594dd4e1c910579b998de29bd", + "ec2f50127c5f461b892cd1cce3823dec", + "ad2d2da8f0c942508463f22179819ad9", + "93604978de264369af6cad9606e3af9e", + "e82012bce3b04a60926f6bccf50b0c5e", + "0729eec1581f4b4197f59906685ab73a", + "2dd4d35ebc8c4b9dafedda3b546fc11d", + "48e600d9711540568e4de524e2993f4d", + "b336bc5c759e443184406160a1ded0ce", + "4dcf7eb450a44f3aa98730b43030b0ac", + "346e0fb21dac44c9ac77bd495244183c", + "dd0a074bd2d6426e9aea514e0a96a078", + "1f56cf9a7d5b4ba4889f302c909b889f", + "9f5f8656edca4ab693c662d7f2bf75ec", + "08a797751c55468aac4bdfbfee0d4d37", + "4e3220619a2b4c358a9772de3a399bec", + "180ada3f5686478caff55bea4844ad82", + "606541fab5144826b8ec524b52793023", + "d9dc0681f3ec4f0cbb82562967f126d5", + "ae30150e12df4320b9048cb03354ef5f", + "362043576d8444ccbe7dfb2796fb2511", + "8144ecf2be9642308f86c57418490d18", + "7047676eed1142a5bf43d6bdced7e8c2", + "252d318144ca4a2386ae149cd4995107", + "0c32754d08584330abdf057ef5fde573", + "81324aba1d6a4a919aeddf13ace64b4a", + "a17391b90bc64d49b80922037401fb64", + "4da1b4471c194bda9ea105fb25769eac", + "5cd3b1a4fbcc4f0a910c5f1e1e250bf0", + "624bb914918f4f1b972d0310030cc29a", + "5797f033785c486ea415d76938605ed2", + "36b3dd197efe44eb8d111b1ab1e31585", + "a696bd2c5a8e464494112c298946b3e1", + "02bd1f7a767140ad9b9aa00b0f310b2b", + "d397a857b7174694bf35f6a0bf258509", + "a45de69949ff4a81a6fd4604a2b4678e", + "ab907069ad414d02952a30a0a014cb73", + "2bd50972a522409398a22ae76b74eb15", + "7c1a57d1a6c243a09ce43e68669ad757", + "c9f2a0612c144ab5a941084b762ce3e2", + "a5cf1251bf4c4023b6dc3e1878740085", + "46e335b5ef4e4dc19d4812b214392e16", + "1731cb921abc41acb30cf18028cf95f9", + "08ddb7a3a5094d5ea3de669f0e03d462", + "56c770bcdb944fbe86a159b9234e778e", + "a6d76986d203472190863f12fcf83e7b", + "f785f0b858d84f2385bd94e8d67cddf0", + "f173fc0b2cd740be83d262e1bf9dbe08", + "6a0c785835d64ccfbe5c97f1ed647893", + "e3a77622925b4ce4986af76f3fb292ed", + "c1e39012677246a09e42d20733c9e174", + "fc00ecc3f0b04687ab7ae379ff2d2bdc", + "265f3a5a0b2d4cabab4f78bdedd6b957", + "15f33f0f77c641aba861879ddb66f655", + "6037d1b48d6144b7a23c2422d07f2184", + "71d9920f7c004a36908c35653c072690", + "233da03e51fc4abd835b4f740e49ac92", + "559305d2cbbb4df3a8cf9f5fd3c2a43d", + "a772ad500ba440169299ebfa61f19cd2", + "a687ec74cac4401f81ea29ee51db6cf4", + "0885d35288444f80a4472c4dc759cb1b", + "184dbc08613944b2bc40e90fe33d99b9", + "6ce16005dd4941f296cfc225e65e6517", + "45458cf2b8694deba8736af6fd57e8e6", + "81838418eb6743e2b6d4bb274514c157", + "5f6804815ea94c229004b75300d6434e", + "2b83b912332742daa28ab4a657380a40", + "c38dd8628d2e4869a5ce5338c87a638f", + "5ba7e060c17f4c509c4e258e3a5e3211", + "31cf47d5aec34a2b97d9efbba84da4e5", + "fd2a719ccbb44d769fa40282835f8286", + "75f4741409a24842895649af0218d239", + "87b474e2d1a9424fa44498e1a09e4600", + "cf00d92f58174d279f65b2fa9e6fc29f", + "2d45fecd748e43b899dd99f00b436ed6", + "8cefe6b259324768837e84e704862876", + "733b3148561e4b67aff5cfe82c168bb2", + "44d4522449874c7cab58e83fd7627903", + "8394d3bb4d60475f9e64f78c3dc4c56a", + "cec143ff825146659d71076cc4f238a5", + "b529628f69c5467294a9bd230210f1dd", + "a1d1a045be664a1784191d2f4dfb12e5", + "1b2837fb0147452a937b35ab8c585ade", + "4aa823c0e15e40b8b328f6f9de3f7771", + "06118d97f4ec45559a7851005f1cb4e1", + "e986b132445440429a6d77bebf031eea", + "18106bb7478c492e982b2485055d12a5", + "e9445fdbd5f640e2b7dacb72d3428057", + "cf3b4bfb03324c5a981041f866aed437", + "9cb0c3e048bc467fa53e4dc1383337fb", + "6aefe246643c479280124257988487d0", + "f35ea75c598e41eb9953f3f908577faa", + "34e150d0acb748da98fb50f5372c57bc", + "60770906b5b14eefa6e2c35a4993f944", + "91f21566f5d34ca785a18b3b372a510c", + "116c89c60a014cadbb281ba93a021cb7", + "e5939b22aecd47168c7e7da7daec611e", + "c66628bfed614e039314a2ab42340d6f", + "e0425d229f5f462e9367c09524e6545f", + "0700a6f52fcd48d99f2c995b9474c963", + "873a8cdf09734e7882ee1503e2aeb7c1", + "ed0cc53b0df34c0e8bf230bc68ae4410", + "ece91278c6df45d39e347a02842173fa", + "10eb570b260b44e6a7d42b6332b897bf", + "6027176c5c824977be67e7a135c0184b", + "49a00770f8fe4ac195311535ee4901af", + "c3e6c70c163a45439ba12ef559a0e6c6", + "cbc8acacf1024ebfb8db8fec7d3601ce", + "6a1718c4158a4df1a4cafba544727cca", + "d0b16c25ae5546a5ba491ff761206ae5", + "9f66fffe75b54a5c8a0d0cd0647460d4", + "851db003a3f44e609e2a50555ed15a6b" + ] + }, + "executionInfo": { + "elapsed": 602984, + "status": "ok", + "timestamp": 1759634516171, + "user": { + "displayName": "thinsri@gmx.de", + "userId": "18422164867366802681" + }, + "user_tz": -120 + }, + "id": "8KT2cbPEBDdZ", + "outputId": "bc2ca7a3-6191-4fef-83fb-b34926dec936" + }, + "outputs": [], + "source": [ + "# test basic functions\n", + "tokenizer, model = load_model(LLAMA)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 5686, + "status": "ok", + "timestamp": 1759634552750, + "user": { + "displayName": "thinsri@gmx.de", + "userId": "18422164867366802681" + }, + "user_tz": -120 + }, + "id": "aa1WGIn8BYzy", + "outputId": "207a2342-ce36-40e1-e2bc-d57dc599f323" + }, + "outputs": [], + "source": [ + "system_message = [{\"role\": \"system\", \"content\": \"You are a helpful assistant.\"}]\n", + "user_prompt = \"Tell me 4 line poem.\"\n", + "messages = system_message + [{\"role\": \"user\", \"content\": user_prompt}]\n", + "generate_stream_with_thread(messages, tokenizer, model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 8333, + "status": "ok", + "timestamp": 1759465246704, + "user": { + "displayName": "thinsri@gmx.de", + "userId": "18422164867366802681" + }, + "user_tz": -120 + }, + "id": "uT_h9S131QBF", + "outputId": "16203a12-f311-496c-9962-d1d72f5cf290" + }, + "outputs": [], + "source": [ + "# prototype system- and user-prompt for synthetic dataset generator\n", + "dataset_types = [\"Instruction-Response Pairs\", \"Multi-Turn Chat Dialogue\", \"Text Classification\"]\n", + "response_csv_columns_headers = {\n", + " \"Instruction-Response Pairs\": [\"instruction\", \"response\", \"domain\", \"complexity\"],\n", + " \"Multi-Turn Chat Dialogue\": [\"conversation_id\", \"turn_number\", \"role\", \"content\"],\n", + " \"Text Classification\": [\"text\", \"label\", \"sourcestyle\"],\n", + "}\n", + "\n", + "dataset_type = \"Text Classification\"\n", + "system_message = f\"You are a dataset generator for {dataset_type}. Respond in csv format only, include the header, nothing extra and use the following columns {response_csv_columns_headers[dataset_type]} .\"\n", + "\n", + "target_domain = \"Cooking\"\n", + "instruction_type = \"Summarization\"\n", + "diversity_prompt = \"Increase in complexity\"\n", + "number_of_samples = 5\n", + "user_message_instruction_response_pairs = f\"For the target domain {target_domain} using the instruction type {instruction_type} create a {dataset_type} dataset. Make the question {diversity_prompt}. Create {number_of_samples} samples.\"\n", + "\n", + "scenario_role=\"customer support\"\n", + "number_of_turns=3\n", + "conversation_goal=\"Explain a complex concept\"\n", + "user_message_multi_turn_chat_dialogue = f\"For the scenario role {scenario_role} with {number_of_turns} number of turns and the conversation goal {conversation_goal} create a {dataset_type} dataset. Create {number_of_samples} samples.\"\n", + "\n", + "text_type = \"News Headlines\"\n", + "list_of_labels = [\"Sports\", \"Politics\", \"Tech\"]\n", + "user_message_text_classification = f\"For the text-type {text_type} and the labels {list_of_labels} create a {dataset_type} dataset. Create {number_of_samples} samples.\"\n", + "\n", + "messages = [{\"role\": \"system\", \"content\": system_message}]\n", + "messages.append({\"role\": \"user\", \"content\": user_message_text_classification})\n", + "response = generate_stream_with_thread(messages, tokenizer, model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 51, + "status": "ok", + "timestamp": 1759347628652, + "user": { + "displayName": "thinsri@gmx.de", + "userId": "18422164867366802681" + }, + "user_tz": -120 + }, + "id": "koJHoRuVDLFN", + "outputId": "08e302a8-7a57-448c-8c02-9d03218b7343" + }, + "outputs": [], + "source": [ + "print(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iObFAoLBwL8F" + }, + "source": [ + "## 2. Gradio App" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0GwRK-ecDPj4" + }, + "outputs": [], + "source": [ + "dataset_types = [\"Instruction-Response Pairs\", \"Multi-Turn Chat Dialogue\", \"Text Classification\"]\n", + "response_csv_columns_headers = {\n", + " \"Instruction-Response Pairs\": [\"instruction\", \"response\", \"domain\", \"complexity\"],\n", + " \"Multi-Turn Chat Dialogue\": [\"conversation_id\", \"turn_number\", \"role\", \"content\"],\n", + " \"Text Classification\": [\"text\", \"label\", \"sourcestyle\"],\n", + "}\n", + "\n", + "def update_params(dataset_type):\n", + " # Default to hidden for all containers\n", + " hide_all = gr.update(visible=False)\n", + "\n", + " ir_update = hide_all\n", + " mtd_update = hide_all\n", + " tc_update = hide_all\n", + "\n", + " # Set the relevant container to visible based on selection\n", + " if dataset_type == \"Instruction-Response Pairs\":\n", + " ir_update = gr.update(visible=True)\n", + " elif dataset_type == \"Multi-Turn Chat Dialogue\":\n", + " mtd_update = gr.update(visible=True)\n", + " elif dataset_type == \"Text Classification\":\n", + " tc_update = gr.update(visible=True)\n", + "\n", + " # Now, we only return 3 updates!\n", + " return ir_update, mtd_update, tc_update\n", + "\n", + "def combine_messages(system_message, user_message):\n", + " messages = [{\"role\": \"system\", \"content\": system_message}]\n", + " messages.append({\"role\": \"user\", \"content\": user_message})\n", + " return messages\n", + "\n", + "def create_download_link(full_response):\n", + " # Create a temporary file\n", + " try:\n", + " temp_file = tempfile.NamedTemporaryFile(\n", + " mode='w',\n", + " delete=False,\n", + " suffix='.csv',\n", + " encoding='utf-8'\n", + " )\n", + "\n", + " temp_file.write(full_response)\n", + " temp_file.close()\n", + " return full_response, gr.update(visible=True, value=temp_file.name)\n", + " except Exception as e:\n", + " error_message = f\"Error writing to file: {e}\"\n", + " return full_response + \"\\n\\n\" + error_message, None\n", + "\n", + "def create_download_link_in_memory(full_response):\n", + " csv_buffer = io.StringIO(full_response)\n", + " csv_buffer.name = \"generated_dataset.csv\"\n", + "\n", + " try:\n", + " return full_response, gr.update(visible=True, value=csv_buffer)\n", + " except Exception as e:\n", + " error_message = f\"Error creating in-memory file: {e}\"\n", + " return full_response + \"\\n\\n\" + error_message, gr.update(value=None, visible=False)\n", + "\n", + "\n", + "def create_dataset_ir(dataset_type, target_domain, instruction_type, diversity_prompt, number_of_samples):\n", + " global response_csv_columns_headers\n", + " system_message = f\"You are a dataset generator for Instruction-Response Pairs. Respond in csv format only, include the header, nothing extra and use the following columns {response_csv_columns_headers[dataset_type]} .\"\n", + " user_message = f\"For the target domain {target_domain} using the instruction type {instruction_type} create a {dataset_type} dataset. Make the question {diversity_prompt}. Create {number_of_samples} samples.\"\n", + " messages = combine_messages(system_message, user_message)\n", + " stream_generator = generate_stream_with_thread_gradio(messages, tokenizer, model)\n", + " full_response = \"\"\n", + " for update in stream_generator:\n", + " full_response = update\n", + " yield update, gr.update(visible=False) # Keep download link hidden during stream\n", + " # After streaming is complete, create the download file\n", + " try:\n", + " temp_file = tempfile.NamedTemporaryFile(\n", + " mode='w',\n", + " delete=False,\n", + " suffix='.csv',\n", + " encoding='utf-8'\n", + " )\n", + " temp_file.write(full_response)\n", + " temp_file.close()\n", + "\n", + " # Final yield with the complete text and the visible download link\n", + " yield full_response, gr.update(visible=True, value=temp_file.name)\n", + " except Exception as e:\n", + " error_message = f\"Error writing to file: {e}\"\n", + " yield full_response + \"\\n\\n\" + error_message, gr.update(visible=False)\n", + "\n", + "\n", + "def create_dataset_mtd(dataset_type, scenario_role, number_of_turns, conversation_goal, number_of_samples):\n", + " global response_csv_columns_headers\n", + " system_message = f\"You are a dataset generator for Multi-Turn Chat Dialogue. Respond in csv format only, include the header, nothing extra and use the following columns {response_csv_columns_headers[dataset_type]} .\"\n", + " user_message = f\"For the scenario role {scenario_role} with {number_of_turns} number of turns and the conversation goal {conversation_goal} create a {dataset_type} dataset. Create {number_of_samples} samples.\"\n", + " messages = combine_messages(system_message, user_message)\n", + " stream_generator = generate_stream_with_thread_gradio(messages, tokenizer, model)\n", + " full_response = \"\"\n", + " for update in stream_generator:\n", + " full_response = update\n", + " yield update, gr.update(visible=False) # Keep download link hidden during stream\n", + " try:\n", + " temp_file = tempfile.NamedTemporaryFile(\n", + " mode='w',\n", + " delete=False,\n", + " suffix='.csv',\n", + " encoding='utf-8'\n", + " )\n", + " temp_file.write(full_response)\n", + " temp_file.close()\n", + "\n", + " # Final yield with the complete text and the visible download link\n", + " yield full_response, gr.update(visible=True, value=temp_file.name)\n", + " except Exception as e:\n", + " error_message = f\"Error writing to file: {e}\"\n", + " yield full_response + \"\\n\\n\" + error_message, gr.update(visible=False)\n", + "\n", + "def create_dataset_tc(dataset_type, text_type, list_of_labels, number_of_samples):\n", + " global response_csv_columns_headers\n", + " system_message = f\"You are a dataset generator for Text Classification. Respond in csv format only, include the header, nothing extra and use the following columns {response_csv_columns_headers[dataset_type]} .\"\n", + " user_message = f\"For the text-type {text_type} and the labels {list_of_labels} create a {dataset_type} dataset. Create {number_of_samples} samples.\"\n", + " messages = combine_messages(system_message, user_message)\n", + " stream_generator = generate_stream_with_thread_gradio(messages, tokenizer, model)\n", + " full_response = \"\"\n", + " for update in stream_generator:\n", + " full_response = update\n", + " yield update, gr.update(visible=False) # Keep download link hidden during stream\n", + " try:\n", + " temp_file = tempfile.NamedTemporaryFile(\n", + " mode='w',\n", + " delete=False,\n", + " suffix='.csv',\n", + " encoding='utf-8'\n", + " )\n", + " temp_file.write(full_response)\n", + " temp_file.close()\n", + "\n", + " # Final yield with the complete text and the visible download link\n", + " yield full_response, gr.update(visible=True, value=temp_file.name)\n", + " except Exception as e:\n", + " error_message = f\"Error writing to file: {e}\"\n", + " yield full_response + \"\\n\\n\" + error_message, gr.update(visible=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 680 + }, + "executionInfo": { + "elapsed": 82700, + "status": "ok", + "timestamp": 1759468954578, + "user": { + "displayName": "thinsri@gmx.de", + "userId": "18422164867366802681" + }, + "user_tz": -120 + }, + "id": "lqrDsgEiwL_n", + "outputId": "81e41895-7c64-44ad-b05f-9b4b92030389" + }, + "outputs": [], + "source": [ + "import gradio as gr\n", + "\n", + "with gr.Blocks() as demo:\n", + " # 1. Main Selection\n", + " with gr.Row():\n", + " with gr.Column():\n", + " dataset_type = gr.Dropdown(\n", + " choices=dataset_types,\n", + " label=\"Dataset Type\",\n", + " value=None,\n", + " )\n", + "\n", + " with gr.Column(visible=False) as ir_container:\n", + " with gr.Row():\n", + " target_domain = gr.Textbox(label=\"Target Domain\")\n", + " instruction_type = gr.Textbox(label=\"Instruction Type\")\n", + " diversity_prompt = gr.Textbox(label=\"Diversity Prompt\")\n", + " number_of_samples_ir = gr.Textbox(label=\"Number of Samples\")\n", + " with gr.Row():\n", + " submit_ir = gr.Button(\"Submit\")\n", + "\n", + " ir_output = gr.Textbox(label=\"AI Response\", lines=10)\n", + " ir_download_link = gr.File(label=\"Download Generated Dataset (.csv)\", visible=False)\n", + "\n", + " with gr.Column(visible=False) as mtd_container:\n", + " with gr.Row():\n", + " scenario_role = gr.Textbox(label=\"Scenario Role\")\n", + " number_of_turns = gr.Textbox(label=\"Number of Turns\")\n", + " conversation_goal = gr.Textbox(label=\"Conversation Goal\")\n", + " number_of_samples_mtd = gr.Textbox(label=\"Number of Samples\")\n", + " with gr.Row():\n", + " submit_mtd = gr.Button(\"Submit\")\n", + "\n", + " mtd_output = gr.Textbox(label=\"AI Response\", lines=10)\n", + " mtd_download_link = gr.File(label=\"Download Generated Dataset (.csv)\", visible=False)\n", + "\n", + " with gr.Column(visible=False) as tc_container:\n", + " with gr.Row():\n", + " text_type = gr.Textbox(label=\"Text Type\")\n", + " list_of_labels = gr.Textbox(label=\"List of Labels\")\n", + " number_of_samples_tc = gr.Textbox(label=\"Number of Samples\")\n", + " with gr.Row():\n", + " submit_tc = gr.Button(\"Submit\")\n", + "\n", + " tc_output = gr.Textbox(label=\"AI Response\", lines=10)\n", + " tc_download_link = gr.File(label=\"Download Generated Dataset (.csv)\", visible=False)\n", + "\n", + " # Attach the listener\n", + " dataset_type.change(update_params, inputs=[dataset_type], outputs=[ir_container, mtd_container, tc_container])\n", + " submit_ir.click(create_dataset_ir, inputs=[dataset_type, target_domain, instruction_type, diversity_prompt, number_of_samples_ir], outputs=[ir_output, ir_download_link])\n", + " submit_mtd.click(create_dataset_mtd, inputs=[dataset_type, scenario_role, number_of_turns, conversation_goal, number_of_samples_mtd], outputs=[mtd_output, mtd_download_link])\n", + " submit_tc.click(create_dataset_tc, inputs=[dataset_type, text_type, list_of_labels, number_of_samples_tc], outputs=[tc_output, tc_download_link])\n", + "\n", + "\n", + "demo.launch(debug=True, share=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QnbU-q7d2oGB" + }, + "source": [ + "## Refactored Code and named temp-file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 680 + }, + "executionInfo": { + "elapsed": 75622, + "status": "ok", + "timestamp": 1759636078533, + "user": { + "displayName": "thinsri@gmx.de", + "userId": "18422164867366802681" + }, + "user_tz": -120 + }, + "id": "9sSdP7hy2oLt", + "outputId": "d99aea8e-feeb-4c9d-d2ee-4d090b295128" + }, + "outputs": [], + "source": [ + "import tempfile\n", + "import os\n", + "import gradio as gr\n", + "\n", + "dataset_types = [\"Instruction-Response Pairs\", \"Multi-Turn Chat Dialogue\", \"Text Classification\"]\n", + "\n", + "response_csv_columns_headers = {\n", + " \"Instruction-Response Pairs\": [\"instruction\", \"response\", \"domain\", \"complexity\"],\n", + " \"Multi-Turn Chat Dialogue\": [\"conversation_id\", \"turn_number\", \"role\", \"content\"],\n", + " \"Text Classification\": [\"text\", \"label\", \"sourcestyle\"],\n", + "}\n", + "\n", + "# System message templates for each dataset type\n", + "system_message_templates = {\n", + " \"Instruction-Response Pairs\": \"You are a dataset generator for Instruction-Response Pairs. Respond in csv format only, include the header, nothing extra and use the following columns {columns}.\",\n", + " \"Multi-Turn Chat Dialogue\": \"You are a dataset generator for Multi-Turn Chat Dialogue. Respond in csv format only, include the header, nothing extra and use the following columns {columns}.\",\n", + " \"Text Classification\": \"You are a dataset generator for Text Classification. Respond in csv format only, include the header, nothing extra and use the following columns {columns}.\",\n", + "}\n", + "\n", + "# User message templates for each dataset type\n", + "user_message_templates = {\n", + " \"Instruction-Response Pairs\": \"For the target domain {target_domain} using the instruction type {instruction_type} create a {dataset_type} dataset. Make the question {diversity_prompt}. Create {number_of_samples} samples.\",\n", + " \"Multi-Turn Chat Dialogue\": \"For the scenario role {scenario_role} with {number_of_turns} number of turns and the conversation goal {conversation_goal} create a {dataset_type} dataset. Create {number_of_samples} samples.\",\n", + " \"Text Classification\": \"For the text-type {text_type} and the labels {list_of_labels} create a {dataset_type} dataset. Create {number_of_samples} samples.\",\n", + "}\n", + "\n", + "def update_params(dataset_type):\n", + " \"\"\"Update visibility of parameter containers based on selected dataset type\"\"\"\n", + " hide_all = gr.update(visible=False)\n", + "\n", + " visibility_map = {\n", + " \"Instruction-Response Pairs\": (gr.update(visible=True), hide_all, hide_all),\n", + " \"Multi-Turn Chat Dialogue\": (hide_all, gr.update(visible=True), hide_all),\n", + " \"Text Classification\": (hide_all, hide_all, gr.update(visible=True)),\n", + " }\n", + "\n", + " return visibility_map.get(dataset_type, (hide_all, hide_all, hide_all))\n", + "\n", + "def combine_messages(system_message, user_message):\n", + " \"\"\"Combine system and user messages into a message list\"\"\"\n", + " return [\n", + " {\"role\": \"system\", \"content\": system_message},\n", + " {\"role\": \"user\", \"content\": user_message}\n", + " ]\n", + "\n", + "def create_dataset(dataset_type, **kwargs):\n", + " \"\"\"\n", + " Generic dataset creation function that handles all dataset types\n", + "\n", + " Args:\n", + " dataset_type: Type of dataset to generate\n", + " **kwargs: Dynamic parameters based on dataset type\n", + " \"\"\"\n", + " # Get the appropriate columns for this dataset type\n", + " columns = response_csv_columns_headers[dataset_type]\n", + "\n", + " # Create system message\n", + " system_message = system_message_templates[dataset_type].format(columns=columns)\n", + "\n", + " # Create user message with appropriate template and parameters\n", + " user_message = user_message_templates[dataset_type].format(\n", + " dataset_type=dataset_type,\n", + " **kwargs\n", + " )\n", + "\n", + " # Combine messages\n", + " messages = combine_messages(system_message, user_message)\n", + "\n", + " # Generate stream\n", + " stream_generator = generate_stream_with_thread_gradio(messages, tokenizer, model)\n", + " full_response = \"\"\n", + "\n", + " # Stream the text output\n", + " for update in stream_generator:\n", + " full_response = update\n", + " yield update, gr.update(visible=False) # Keep download hidden during stream\n", + "\n", + " # After streaming is complete, create the download file\n", + " try:\n", + " temp_filename = f\"generated_dataset_{dataset_type}.csv\"\n", + " temp_dir = tempfile.gettempdir()\n", + " temp_filepath = os.path.join(temp_dir, temp_filename)\n", + "\n", + " with open(temp_filepath, 'w', encoding='utf-8') as temp_file:\n", + " temp_file.write(full_response)\n", + "\n", + " # Final yield with the complete text and the visible download link\n", + " yield full_response, gr.update(visible=True, value=temp_file.name)\n", + " except Exception as e:\n", + " error_message = f\"Error writing to file: {e}\"\n", + " yield full_response + \"\\n\\n\" + error_message, gr.update(visible=False)\n", + "\n", + "# Wrapper functions for each dataset type (to handle different parameter names)\n", + "def create_dataset_ir(dataset_type, target_domain, instruction_type, diversity_prompt, number_of_samples):\n", + " \"\"\"Create Instruction-Response Pairs dataset\"\"\"\n", + " yield from create_dataset(\n", + " dataset_type,\n", + " target_domain=target_domain,\n", + " instruction_type=instruction_type,\n", + " diversity_prompt=diversity_prompt,\n", + " number_of_samples=number_of_samples\n", + " )\n", + "\n", + "def create_dataset_mtd(dataset_type, scenario_role, number_of_turns, conversation_goal, number_of_samples):\n", + " \"\"\"Create Multi-Turn Chat Dialogue dataset\"\"\"\n", + " yield from create_dataset(\n", + " dataset_type,\n", + " scenario_role=scenario_role,\n", + " number_of_turns=number_of_turns,\n", + " conversation_goal=conversation_goal,\n", + " number_of_samples=number_of_samples\n", + " )\n", + "\n", + "def create_dataset_tc(dataset_type, text_type, list_of_labels, number_of_samples):\n", + " \"\"\"Create Text Classification dataset\"\"\"\n", + " yield from create_dataset(\n", + " dataset_type,\n", + " text_type=text_type,\n", + " list_of_labels=list_of_labels,\n", + " number_of_samples=number_of_samples\n", + " )\n", + "\n", + "# Gradio UI\n", + "with gr.Blocks() as demo:\n", + " gr.Markdown(\"# Synthetic Dataset Generator\")\n", + "\n", + " # Main Selection\n", + " with gr.Row():\n", + " with gr.Column():\n", + " dataset_type = gr.Dropdown(\n", + " choices=dataset_types,\n", + " label=\"Dataset Type\",\n", + " value=None,\n", + " )\n", + "\n", + " # Instruction-Response Pairs Container\n", + " with gr.Column(visible=False) as ir_container:\n", + " gr.Markdown(\"### Instruction-Response Pairs Parameters\")\n", + " with gr.Row():\n", + " target_domain = gr.Textbox(label=\"Target Domain\")\n", + " instruction_type = gr.Textbox(label=\"Instruction Type\")\n", + " diversity_prompt = gr.Textbox(label=\"Diversity Prompt\")\n", + " number_of_samples_ir = gr.Textbox(label=\"Number of Samples\")\n", + " with gr.Row():\n", + " submit_ir = gr.Button(\"Generate Dataset\")\n", + "\n", + " ir_output = gr.Textbox(label=\"AI Response\", lines=10)\n", + " ir_download_link = gr.File(label=\"Download Generated Dataset (.csv)\", visible=False)\n", + "\n", + " # Multi-Turn Chat Dialogue Container\n", + " with gr.Column(visible=False) as mtd_container:\n", + " gr.Markdown(\"### Multi-Turn Chat Dialogue Parameters\")\n", + " with gr.Row():\n", + " scenario_role = gr.Textbox(label=\"Scenario Role\")\n", + " number_of_turns = gr.Textbox(label=\"Number of Turns\")\n", + " conversation_goal = gr.Textbox(label=\"Conversation Goal\")\n", + " number_of_samples_mtd = gr.Textbox(label=\"Number of Samples\")\n", + " with gr.Row():\n", + " submit_mtd = gr.Button(\"Generate Dataset\")\n", + "\n", + " mtd_output = gr.Textbox(label=\"AI Response\", lines=10)\n", + " mtd_download_link = gr.File(label=\"Download Generated Dataset (.csv)\", visible=False)\n", + "\n", + " # Text Classification Container\n", + " with gr.Column(visible=False) as tc_container:\n", + " gr.Markdown(\"### Text Classification Parameters\")\n", + " with gr.Row():\n", + " text_type = gr.Textbox(label=\"Text Type\")\n", + " list_of_labels = gr.Textbox(label=\"List of Labels\")\n", + " number_of_samples_tc = gr.Textbox(label=\"Number of Samples\")\n", + " with gr.Row():\n", + " submit_tc = gr.Button(\"Generate Dataset\")\n", + "\n", + " tc_output = gr.Textbox(label=\"AI Response\", lines=10)\n", + " tc_download_link = gr.File(label=\"Download Generated Dataset (.csv)\", visible=False)\n", + "\n", + " # Event Handlers\n", + " dataset_type.change(\n", + " update_params,\n", + " inputs=[dataset_type],\n", + " outputs=[ir_container, mtd_container, tc_container]\n", + " )\n", + "\n", + " submit_ir.click(\n", + " create_dataset_ir,\n", + " inputs=[dataset_type, target_domain, instruction_type, diversity_prompt, number_of_samples_ir],\n", + " outputs=[ir_output, ir_download_link],\n", + " )\n", + "\n", + " submit_mtd.click(\n", + " create_dataset_mtd,\n", + " inputs=[dataset_type, scenario_role, number_of_turns, conversation_goal, number_of_samples_mtd],\n", + " outputs=[mtd_output, mtd_download_link],\n", + " )\n", + "\n", + " submit_tc.click(\n", + " create_dataset_tc,\n", + " inputs=[dataset_type, text_type, list_of_labels, number_of_samples_tc],\n", + " outputs=[tc_output, tc_download_link],\n", + " )\n", + "\n", + "# Launch with share=True for Google Colab\n", + "demo.launch(debug=True, share=True)" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "authorship_tag": "ABX9TyONLEGpVh5gB9OpjnkJUf32", + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "02bd1f7a767140ad9b9aa00b0f310b2b": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + 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