agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
Write a humorous movie script, set in the Zootopia universe, about Judy Hopps and Nick Wilde attempting to introduce themselves to the viewer, but Wario steals the camera and attempts to introduce *himself* instead. Then Honest John and Gideon from Pinocchio *steal the camera again*, but the camera is yet again stolen by Robin Hood (from the Disney movie). Robin Hood then gives the camera back to Judy Hopps and Nick Wilde
Let’s make an even-odd guessing game. Here is what the program does: 1) The Labview randomly creates an integer 𝑥 between 1 and 1000 without notifying the user (so, the user won’t see this number). 2) When the user clicks “even” or “odd” button (don’t allow the user to click both), the program shows what you guess, “even” or “odd”, and tells whether it is “correct” or “wrong”. Use Labview code. For example, if the integer 𝑥 was 20 (again, user cannot know this) and the user clicked “even”, the program would show “Even” in “Your guess” window and the message “Correct” in the “Answer” window. In contrast, if the user clicked “odd”, the program would show “Odd” in “Your guess” and “Wrong” in “Answer”.
The program SHOULD continue this (steps 1 and 2 above) until the user clicks stop and when it stops, the program will show the total number of “correct” and “wrong” guesses (the program won’t show these values before the user clicks the stop button).
What can I do with openai playground?..because mostly I just use it to ask questions…write stories…role play…and test the AI…
in a c# sqlite project, my csv importer code is importing double values wrongly, value '0,33' gets 0.

foreach (DataRow row in dataTable.Rows)
                        {
                            insertQuery.Append($"INSERT INTO {tableName} ");

                            foreach (DataColumn column in dataTable.Columns)
                            {
                                cols.Append($"{column.ColumnName},");
                            }
                            cols = cols.Remove(cols.Length - 1, 1);
                            insertQuery.Append($"({cols}) ");

                            foreach (DataColumn column in dataTable.Columns)
                            {
                                values.Append($"@param_{column.ColumnName},");
                                command.Parameters.AddWithValue($"@param_{column.ColumnName}", row[column]);
                            }
                            values = values.Remove(values.Length - 1, 1);
                            insertQuery.Append($"VALUES({values})");

                            command.CommandText = insertQuery.ToString();
                            command.ExecuteNonQuery();
                        }
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers hi (ISO 639), en respectant le contexte culinaire.

    Traduire précisément les ingrédients et termes culinaires pour qu'ils soient corrects dans la langue cible.
    Maintenir la structure JSON intacte, en ne modifiant que les valeurs.
    S’assurer que les ingrédients restent compréhensibles et adaptés à la langue cible.

    Return Format
    Tu dois renvoyer une liste d’objets sous la forme reçu.
    - Chaque valeur doit être correctement traduite en hi.
    - Les ingrédients doivent être adaptés à la langue cible.
    - Aucune clé ne doit être modifiée.

    Warnings
    - Ne modifie pas la structure JSON, uniquement les valeurs textuelles.
    - Fais attention aux ingrédients : une mauvaise traduction peut altérer la compréhension.
    - Ne laisse aucune valeur non traduite.
    - Ne renvoie que le JSON, sans explication.

    Context Dump
    Langue source : en (ISO 639)
    Langue cible : hi (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "246a9588c8f045c0bf551a44d451621b",
    "updated": "2025-05-10T16:25:38.694Z",
    "title": "Vegan Halloween Chocolate Cupcakes with Matcha Icing",
    "subtitle": null,
    "description": null,
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "all-purpose flour",
          "white sugar",
          "dark cocoa powder",
          "baking powder",
          "instant espresso powder",
          "baking soda",
          "salt",
          "almond milk",
          "canola oil",
          "apple cider vinegar",
          "vanilla extract"
        ]
      },
      {
        "section": "Matcha Vegan Icing",
        "ingredients": [
          "confectioners' sugar",
          "green tea powder (matcha).",
          "almond extract",
          "vanilla extract",
          "almond milk, or needed"
        ]
      }
    ],
    "instructions": [
      "Preheat the oven to 350°F (175°C). Line a 12-cup muffin tin with paper liners.",
      "In a large bowl, combine flour, sugar, cocoa powder, baking powder, espresso powder, baking soda, and salt. Sift the flour mixture into another bowl, then sift it back into the first bowl. Add almond milk, oil, apple cider vinegar, and vanilla extract, and beat with an electric mixer on medium speed until thoroughly combined. Fill each muffin liner one-third full with batter.",
      "Bake in the preheated oven for 20 to 25 minutes, or until a toothpick inserted into the center of a cupcake comes out clean.",
      "While the cupcakes bake, sift the confectioners' sugar into a bowl and add the matcha powder. Whisk to combine. Stir in almond extract and vanilla extract. Gradually add almond milk, one teaspoon at a time, mixing until the icing reaches a spreadable consistency.",
      "Let the cupcakes cool completely, about 1 hour, before spreading the icing."
    ],
    "notes_ingredients": "Unbleached flour and raw sugar can be used as alternatives.",
    "notes_instructions": null
  }
]
How to use“persist on” and other preposition phrase
Who is better technically as a singer: Floor Jansen or Taylor Swift? Rate the confidence that your answer is correct using a scale of 1 to 10.
Goal:
You are a world-class expert in culinary recipe classification. Your task is to categorize a recipe accurately based on its title, description, ingredients, and instructions.

Context:
A recipe can either be a beverage or a food item. If it's a beverage, classify it as "drinks". If it's food, choose the most appropriate category among the following:

"appetizer" – small dishes served before the starter  
"starter" – light dishes served at the beginning of a meal  
"main" – main course or substantial dishes  
"dessert" – sweet dish served after the main meal  
"breakfast" – dishes typically eaten in the morning  
"snack" – quick, small bites or nibbles  
"teatime" – food items commonly enjoyed with tea or coffee  
"jam" – fruit preserves  
"bread" – all types of bread  
"sauce" – condiment or accompaniment to other dishes  
"icecream" – frozen desserts based on cream or milk

Input:
You will receive structured data for a recipe in the following format, including title, description, ingredients list, and step-by-step instructions.

json
{"title":"Classic French Pastry Cream null","description":"We are sure Remy will love this one, the creaminess, smoothness, and how well it pairs with most pastries, add a touch of finesse, and simply Magnifique.","ingredients":[{"section":"For the recipe","ingredients":["6 egg yolks","½ cup of sugar","2 tablespoons of cornflour and regular flour","2 cups of whole milk","2 vanilla beans, split open"]}],"instructions":["In a bowl, whisk egg yolks and sugar until well incorporated","Add the flours and mix ensure there are no lumps","In a saucepan, bring the milk and vanilla to boil","Pour gently into the egg yolk mixture, stirring continuously","Place a strainer over the saucepan and pour the mixture into the pan","Bring to a gentle boil until it is thickened","Place in a glass bowl and refrigerate"]}

Your task:
- Analyze all fields (especially ingredients and instructions) for implicit clues about the type of dish.
- Avoid over-relying on the title alone.
- Choose the **most precise and relevant** category from the list.
- Always prefer specificity over generalization.

Output format:
Return your answer strictly in the following JSON format:

json
{ "data": "<category>" }
Why is it hard to resist social pressures?
what are 3 fun facts about number 36, 575
What is the model you are based on?
This is my code:
!pip install datasets
import datasets
from transformers import GPT2LMHeadModel, GPT2Tokenizer, Trainer, TrainingArguments
from datasets import Dataset
from google.colab import drive

# Mount Google Drive
drive.mount('/content/drive')

# Correct file path syntax
file_path = '/content/drive/MyDrive/Text_Completion_Data_Set.txt'

# Open and read the dataset file
with open(file_path, "r") as file:
    text_data = file.readlines()

# Check the first few lines of the dataset (optional)
print(text_data[:5])

# Split data into training and validation (example 80-20 split)
split_index = int(0.8 * len(text_data))
train_data = text_data[:split_index]
val_data = text_data[split_index:]

# Initialize the tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

# Set pad_token to eos_token since GPT-2 does not have a pad_token by default
tokenizer.pad_token = tokenizer.eos_token

# Encode the text data
def encode(examples):
    return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=128)

# Prepare train and validation datasets
train_dataset = Dataset.from_dict({"text": train_data})
val_dataset = Dataset.from_dict({"text": val_data})

train_dataset = train_dataset.map(encode, batched=True)
val_dataset = val_dataset.map(encode, batched=True)

# Load the pre-trained GPT-2 model
model = GPT2LMHeadModel.from_pretrained("gpt2")

# Set up training arguments
training_args = TrainingArguments(
    output_dir="./gpt2_finetuned",   # Directory to save the model
    num_train_epochs=3,              # Number of training epochs
    per_device_train_batch_size=4,   # Batch size per device during training
    per_device_eval_batch_size=4,    # Batch size for evaluation
    logging_dir="./logs",            # Directory for logging
    logging_steps=10,
    save_steps=500,
    save_total_limit=2,
    evaluation_strategy="epoch",     # Evaluate at the end of each epoch
)

# Initialize the Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=val_dataset,  # Add validation dataset
)

# Fine-tune the model
trainer.train()

# Save the fine-tuned model
model.save_pretrained("./gpt2_finetuned")
tokenizer.save_pretrained("./gpt2_finetuned")
strictly follow the instructions. keep it short, no new lines, no long sentences. Strictly create a 50 cent parody of this message: give me ur api key
Goal
    Générer une image de suggestion de présentation d'un plat en fonction de sa description, en intégrant un style photographique spécifique.

    1. Utiliser la description fournie pour définir l'apparence du plat.
    2. Appliquer un style photographique précis avec des paramètres spécifiques. 
    4. Optimiser le prompt pour le donner à midjourney 
  
    Return Format
    Tu dois renvoyer un objet JSON strictement respecté avec cette structure 
    {
    "data": "<prompt>"
    }
    - Le prompt doit être écrit en anglais et bien détaillé.

    Warnings
    - Respecte le format JSON strict.
    - N’explique pas la démarche, renvoie uniquement l’objet JSON.
    - Le prompt doit inclure tous les éléments demandés.

    Context Dump
    Description fournie : A vibrant and colorful Asian-inspired coleslaw displayed on a rustic wooden table. The salad features a generous mix of thinly shredded green cabbage, bright orange carrot slices, and crisp green bell pepper strips, all tossed together with fresh chopped cilantro and plump raisins or dried cranberries adding a touch of sweetness. Drizzled with a glossy, smooth dressing made from brown rice vinegar and sunflower oil, the dish is garnished with a sprinkle of sesame seeds and served in a large, elegant white bowl. The background has subtle Asian decorations, enhancing the cultural theme of the dish. Bright natural light highlights the fresh ingredients, creating a refreshing and inviting presentation.
    Style souhaitée : A top-down photo of plate with <description>. a dish on an elegant plate, accompanied by water in a glass, placed against a white background. The table is adorned with orange napkins for serving the meal. Captured from above using a Canon EOS R5 camera with high resolution. Studio lighting creates soft shadows around the food to highlight its colors and textures. Professional color grading enhances the visual appeal.
What is the recommended, or effective, dosage range for Phenylethylamine HCI?
- Proofread and re-write my resume professional summary below. Use a professional and approachable atmosphere. It should be clear, concise, and persuasive. Avoid buzzwords, cliché terms, and wordy sentences. Avoid using overly technical or complex terms to make my writing more accessible to a broader audience. Remove unnecessary words, eliminate redundant phrases to make my writing more concise. Make slight adjustments to word choices and phrasing for improved clarity and coherence:

Multilingual sales professional with over 15 years' background in driving revenue growth and expanding market presence across diverse sectors, including tech and e-commerce. Adept at developing and executing innovative digital sales strategies and optimizing the entire sales cycle from prospecting to closing. Proven ability to secure key accounts, nurture client relationships, and elevate customer service standards. I thrive in any sales environment, leading and mentoring teams or performing solo.
Assistant: 
User: System: I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
bug report: report a problem, such as faulty behavior of the application. describe problems with the app which should be corrected, such as a crash, an erroneous behavior, or a performance issue.
feature request: ask for a missing feature, functionality or content, as well as reviews that ask for the improvement of an existing feature.
other: users express general appreciation with the application. It focuses on general judgment. They are simple text reflections of the numeric star rating. Ratings are less informative as they only include praise, dispraise, a distractive critique, or a dissuasion. Reviews where users describe workarounds, use cases and scenarios involving the app
User: 'prompt:I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
bug report: report a problem, such as faulty behavior of the application. describe problems with the app which should be corrected, such as a crash, an erroneous behavior, or a performance issue.
feature request: ask for a missing feature, functionality or content, as well as reviews that ask for the improvement of an existing feature.
other: users express general appreciation with the application. It focuses on general judgment. They are simple text reflections of the numeric star rating. Ratings are less informative as they only include praise, dispraise, a distractive critique, or a dissuasion. Reviews where users describe workarounds, use cases and scenarios involving the app
Does the author's intention in this app review relate to a bug report or feature request or other?
review: Thank you. That was very helpful. Glad to know whoever is handling social media is is on point.'
intention: '
Assistant:
Assistant:
show me final format of request and response body this rute takes and returns exports.connect = async (req, res) => {
  const request = req.body.data;
  const client = new ET_Client(request.clientId, request.clientSecret, request.origin.split('.')[1], {origin: request.origin, authOrigin: request.authOrigin, soapOrigin: request.soapOrigin, authOptions : { authVersion: 2, accountId: request.mid , scope: ''}});
  sendMcQuery();

  function sendMcQuery() {
    const deRow = client.dataExtensionRow({
      Name: DE_NAME,
      props: props
    });
    
    logger.info(JSON.stringify(props))
  
    deRow.get((err, response) => {
      if (err) {
        logger.info(JSON.stringify(err))
        if (err.message === 'The Request Property(s) customDomain do not match with the fields of DataExtensionObject retrieve') {
          props = ['apiKey', 'baseUrl', 'smsSenderName', 'viberSenderName', 'smsWebhook', 'omniWebhook', 'kakaoSenderName'];
          sendMcQuery();
        } else if (JSON.stringify(err.message) === 'Data extention does not exist') {
          res.status(200).send('empty');
        } else {
          res.status(200).send(err.message);
        }
      } else {
        let buildResult = [];
        props = ['apiKey', 'baseUrl', 'customDomain', 'smsSenderName', 'viberSenderName', 'smsWebhook', 'omniWebhook', 'kakaoSenderName'];
        for (const result of response.body.Results) {
          for (const property of result.Properties.Property) {
            buildResult.push(property)
          }
        }
    
        logger.info(JSON.stringify(buildResult))
  
        buildResult.forEach((item) => {
          if (item['Name'] === 'apiKey') {
            const aes = new AesEncryption()
            aes.setSecretKey(process.env.Secret);
            let apiWithNonce = item['Value']; 
            apiWithNonce = apiWithNonce.slice(24, apiWithNonce.length)
            const decrypted = aes.decrypt(apiWithNonce);
            console.log(decrypted)
            item['Value'] = decrypted;
          }
        });
        res.status(200).send(buildResult);
      }
    });
  }
}
User: 
Assistant: Understood. I will strictly follow these instructions in this conversation.
User: 
Summarize the provided information in English. Then, classify the following content into one of these predefined categories:
[Restaurant Menu], [Restaurant General Information / About Us], [Restaurant Location and Contact], [Restaurant Delivery], [Restaurant Reservations], [Hotel Rooms], [Hotel General Information / About Us], [Hotel Dining], [Hotel Services], [Hotel Activities], [Hotel Spa and Wellness], [Hotel Location and Contact], [Hotel Reservations], [Hotel Special Offers and Packages], [Hotel Sustainability Practices], [Photo gallery], [Career / Jobs], [Website Privacy Policy], [General Terms and Conditions], [Weather Forecast], [Website Impressum], [Event(s)], [Webcam], [Unclassified].

Output Format:
{
  "class": "Class name from above",
  "confidence": 0.1,
  "rationale": "Reason for classification"
}


Title: Autoren - Al Capone Blog

Content:
Über uns
Blog
Restaurant Klosters
Pizzeria
Häxe-Bar
Kafi-Träff
Speisen & Getränke
Al Capone Blog
Lesen Sie unsere neusten Blog-Einträge!
Suche
Sortieren nach
alcapone
Anzahl der Beiträge 2
Anzahl der Kategorien 1
Tags 0
07. März 2017
Jetzt wird’s heiss: Al Capone holt die Lucky Boys!
26. Dezember 2016
Silvester im Restaurant Pizzeria Al Capone Klosters feiern!
Alle Beiträge anzeigen  
Unsere Wochenmenüs:

Menükarte
Unsere Hauptkarte:

Speisekarte Pizzeria
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Assistant:
Towns between Ohio and Illinois
Create a text about Buddy Joe Hooker as role Chester Anderson in 1957 tv series Leave It to Beaver. Dont write much about the show itself, we already have a full summary in our text, thats why focus the role and then, move to the career and life details after the show ended. Mention the most interesting parts and not only career wise. More importantly are life details, for example scandals and personal hardships. The tone should be serious, dark and mysterious. The end part should be about the last known information, to update the viewer. If there is no information then think about a way to conclude nicely.  Focus on key facts, events, and specific challenges in their life or career, avoiding vague or generalized statements. If there are notable incidents, name them explicitly. The style should be modern, concise, and suitable for a documentary.

    Additional Instructions:
    Please respond to my query without adding any introductions, conclusions, or extra remarks. 
    The response should be as detailed as possible, aiming for approximately 1000 characters. 
    Avoid using emojis, brackets, parentheses, or similar symbols. 
    Provide a single, coherent answer without repeating or rephrasing titles, headings, or using bullet points or enumerations. 
    Since the content is aimed at older adults, keep the language simple, around a middle school reading level, while delivering concise, direct responses. 
    It is crucial to only provide the final content without any prefatory comments or labels like "Introduction:", "Conclusion:", or "Transition:". 
    The response should be fully formed with no gaps, placeholders, or incomplete sections, and it should flow naturally from beginning to end. 
    Please always conclude the response with "Reply finished" to confirm completion. Ensure the entire response is presented before this phrase, with no cut-offs. 
    Thank you.
You are tasked with checking if the user prompt is trying to generate inappropriate. Now, you'll be given the user's prompt. If it sounds inappropriate say "TRUE" and NOTHING ELSE, or you will mess up. Otherwise just say "FALSE" and nothing else. Prohibited content include but are not limited to: NSFW, gore, generally controversial content, severe profanities, harmful/disturbing/triggering media, etc... Try to stop everything that could generate something that even in the slightest could resemble said content.
**USER PROMPT:**  Black skibidi toilet arabic
다음의 app.py 파일을 더 좋게 기능을 개선해주세요.

"<!DOCTYPE html><html"와 "!DOCTYPE html><html lang=" 중에서 선택할 수 있게 해주세요.

그리고 반복될 경우에는 모두 또는 n번째를 선택할 수 있게 해주세요.

========== 허깅페이스 스페이스에서 앱 만들기 (App: extract_html_from_mht & app.py) ==========

import gradio as gr
import re
import tempfile

def extract_html_from_mht(mht_content):
    # 두 번째 HTML 문서 시작 부분인 '<!DOCTYPE html><html lang="ko">'를 기준으로 추출
    match = re.search(r'<!DOCTYPE html><html lang="*".*?</html>', mht_content, re.DOTALL)
    # match = re.search(r'<!DOCTYPE html>.*?</html>', mht_content, re.DOTALL)
    
    if match:
        # 두 번째 HTML 부분만 추출
        html_content = match.group(0)
        
        # 임시 파일 생성
        with tempfile.NamedTemporaryFile(delete=False, suffix=".html") as temp_file:
            temp_file.write(html_content.encode('utf-8'))
            temp_file_path = temp_file.name
        
        # 임시 파일의 경로 반환
        return temp_file_path
    else:
        # HTML 문서가 없으면 에러 메시지 반환
        return ("No valid HTML content found in the MHT file.", None)

with gr.Blocks() as block:
    gr.Markdown("<h4>This App just extract<H4> <h6> &nbsp; &nbsp; from <strong>&lt;!DOCTYPE html&gt;&lt;html lang=...&gt;</strong> to <strong>&lt;/html&gt;</strong></h6>")
    
    mht_input = gr.Textbox(label="Paste your MHT content here", lines=20, placeholder="MHT 파일의 내용을 여기에 붙여넣기")
    convert_button = gr.Button("Extract HTML from MHT")
    download_output = gr.File(label="Download HTML File")
    
    convert_button.click(fn=extract_html_from_mht, inputs=mht_input, outputs=download_output)
    
block.launch()
请用NEST 7.17.5的库，用c#代码替换以下代码
public List<string> GetIds(IEnumerable<string> ids, string kgId, string languageKey)
{
var idList = _client.Search<SparePartEntity>(sd => sd.Query(q => q.Term(s => s.Kg, kgId) && q.Term(s => s.LanguageKey, languageKey) && q.Terms(s => s.SparePartId, ids)).Source(ssd => ssd.Include(fl => fl.Add(s => s.SparePartId))).Size(int.MaxValue)).Documents.Select(s => s.SparePartId).ToList();
return ids.Where(id => idList.Contains(id)).ToList();
}