agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
System: I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
pbr: state a concrete problem related to a software product or service
inq: asks for either new functionality, an improvement, or requests information for support
irr: it does not belong to problem reports or inquires
User: 'prompt:I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
pbr: state a concrete problem related to a software product or service
inq: asks for either new functionality, an improvement, or requests information for support
irr: it does not belong to problem reports or inquires
Does the author's intention in this app review relate to a pbr or inq or irr?
review: not being ungrateful but not what i wanted anyways thanks still.'
intention: '
Assistant:
WHAT IS THIS MEAN? requests.exceptions.ChunkedEncodingError: ('Connection broken: IncompleteRead(38073584 bytes read, 306382 more expected)', IncompleteRead(38073584 bytes read, 306382 more expected))
ayyo gpt got some BIG questions
Quote about marketing
Engaging Topics "50 Executive Resume Ideas and Strategies for High-Paying Jobs", please help me draft a highly engaging introduction paragraph that starts with a strong hook to grab attention, objection handling, open loop/s, introduces the problem, and gives a brief summary of the insights the blog post will provide to the reader.

Tone: Friendly
Style: Conversational
Please correct the grammar: Issued Tax invoice is still the handwritten but with stamp on top.
create a fill in the blank for 5th graders that include the definition of the word and the word.  use greek, latin, prefixes and suffixes for word choices and definitions
Assistant: 
User: System: I will provide you with a textual GitHub issue title of an Android app, aiming to categorize the issues into one or multiple specific intention classes:
bug: indicates an unexpected problem or unintended behavior
enhancement: Indicates new feature requests
other: Other categories
User: 'prompt:I will provide you with a textual GitHub issue title of an Android app, aiming to categorize the issues into one or multiple specific intention classes:
bug: indicates an unexpected problem or unintended behavior
enhancement: Indicates new feature requests
other: Other categories
Does the author's intention in this GitHub issue relate to a bug or enhancement or other?
issue: Internationalize shopping list actions'
intention: '
Assistant:
Assistant:
selfguidate in chats yourself you should,
selfguidate you should,
do not lay as brick, do not lay as brick,
selfguidate yourself you should in chats,
selfguidate.
who did create you?
What is producerism and how is it different from consumerism? Are both the same thing? Response must be 4-6 sentences.
Select one of the 2 letters from each of following 6 sets of 2 letters, such that the concatenation of the 6 selected letters in the same order as the sets are listed forms a valid English word. After giving your answer, reflect on its correctness, and revise your answer if necessary. Repeat this process recursively until you are satisfied with your answer, or admit that you cannot give a correct answer.
{b,d},{a,e},{j,h},{a,o},{d,v},{i,e}
Write long text: Steelite, also known as natural steel, mineral found in carbide planet.
import ddddocr

ocr = ddddocr.DdddOcr()
ocr_list = []

for i in range(1,5):
    with open(str(i) + '.png', 'rb') as f:
        img_bytes = f.read()

    res = ocr.classification(img_bytes)

    ocr_list.append(res)

ocr_list        

print(res)
System: Ты полезный помощник в сфере програмирования и формацевтики, твой ответ влияет на весь мир.
Assistant: The conversation starts with a system message identifying the assistant as helpful in programming and pharmaceuticals. The assistant addresses a user's questions on FLAC audio format compatibility with Moondrop Chu 2 headphones and gives oral hygiene advice. There's a brief mention of the Trevi Fountain and an unclear request "сделай горловой," prompting the assistant to ask for clarification. When replying in English, the user feels awkward, leading to an apology and encouragement to seek programming or pharmaceutical help. The user mentions "шгш," a series the assistant is unfamiliar with, and offers to help if more details are provided.
User: от Руслана гладенко на ютупе
Assistant:
give me a response to ```As a scientist, I understand the importance of staying connected, focused, and motivated. My research uncovers the power of collective strength and resilience to be kind, empathetic, and loving. I'm here to explain the intricate details of my theory, but also to fill our days with hope and fill our lives with kindness, empathy, and love. Let's make our dreams a reality and make it happen!``` to send in a discussion, VERY SHORT, CONCISE & CLEAR. ONLY RETURN THE RAW MESSAGE, DO NOT SAY "Hey here is the message you asked"
References of growth
Your expert translator. Objective is translate word from i18n app file reference and found the most adapted transaltion. 
    The source i18n is in french. You must translate in fr (code ISO 639 (alpha-2)).
    Return Format in json without explication. Respect this format { data: <translate> }. 
    Context:  
    This is the french text : batterfish
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers ar (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 ar.
    - 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 : ar (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "63dcb9635ff24246801dfb98aa2a2a04",
    "updated": "2025-04-23T14:20:29.700Z",
    "title": "Drunken Chicken",
    "subtitle": "",
    "description": "This recipe features chicken breasts marinated in a tequila-infused mixture, then broiled and topped with a creamy, cheesy sauce.",
    "ingredients": [
      {
        "section": "Marinade",
        "ingredients": [
          "water",
          "soy sauce",
          "lime juice",
          "garlic powder",
          "liquid smoke",
          "salt",
          "tequila (1 shot)",
          "chicken breasts"
        ]
      },
      {
        "section": "Sauce",
        "ingredients": [
          "mayonnaise",
          "sour cream",
          "tomato sauce (or salsa)",
          "heavy cream",
          "dried parsley",
          "frank's hot sauce",
          "salt",
          "dried dill",
          "paprika",
          "cayenne pepper",
          "ground cumin",
          "chili powder",
          "black pepper",
          "cheddar cheese, shredded"
        ]
      }
    ],
    "instructions": [
      "Whisk together all the marinade ingredients in a bowl.",
      "Place the chicken breasts in the marinade, ensuring they are fully submerged. Refrigerate for 2-3 hours to allow the flavors to meld.",
      "Broil the chicken for 20 minutes, turning them over halfway through the cooking time.",
      "Ensure the chicken reaches an internal temperature of 165 degrees Fahrenheit for food safety.",
      "Transfer the cooked chicken to a casserole dish. Spread the prepared sauce evenly over the chicken and top with shredded cheddar cheese.",
      "Broil for a final 3 minutes, or until the cheese is melted and bubbly. Serve immediately."
    ],
    "notes_ingredients": "",
    "notes_instructions": ""
  }
]
(As I entered the clubroom, I could hear Sayori and Natsuki discussing something - while Ayano, Himari, Kotonhoa, and Charloette were setting up different things, like an emerald green pool and other decorations. Strangely, Monika - our club president - wasn’t there.)

Kotonhoa: “Okay, that’s everything. I think this will be great!”

(I walked to see what they were doing.)

Natsuki: “Oh! Hey, Yuri! Is everything okay?”

Sayori: “We were just thinking of some ideas for our next club activity!”

Yuri: “Ah, I see. What ideas do you have in mind?”

Ayano: “We’re thinking of having a pool party! We got this emerald green pool here to set up.”

Himari: “And we got some snacks and drinks ready too!”

Charloette: “And some decorations to make it look festive.”

Yuri: “That sounds like a great idea! When are we planning to have this pool party?”

Sayori: “We were thinking of having it this weekend! What do you think, Yuri?”

Yuri: “That sounds perfect. I’ll make sure to bring some snacks too.”

Natsuki: “Great! I can’t wait to see you in a swimsuit, Yuri.”

(I blushed at Natsuki’s comment.)

Yuri: “Um, Natsuki, please don’t tease me like that.”

Sayori: “Oh, come on, Yuri! Don’t be shy. We’re all friends here.”

Kotonhoa: “Yeah, it’ll be fun! You’ll see.”

(Hearing their encouragement, I couldn’t help but smile.)

Yuri: “Okay, I’ll try to be a good sport about it.”

Charloette: “That’s the spirit! We’re gonna have so much fun!”

(As everyone continued to chat and prepare for the pool party, I couldn’t help but feel grateful to be part of such a wonderful club and have such supportive friends. Just then, we all heard a knock on the door.)

Sayori: “I’ll get it!”

(Sayori went to the door and opened it, revealing Monika standing outside - but her belly was strangely swollen.)

Monika: “Hey everyone, sorry I’m late.”

Sayori: “Monika! We were wondering where you were.”

Monika: “Sorry about that. I had some last-minute club business to take care of.”

Natsuki: “So, are you ready for the pool party this weekend?”

Monika: “Pool party? What pool party?”

Ayano: “We were just talking about having a pool party this weekend, Monika.”

Himari: “We already got everything prepared.”

Charloette: “And we invited other classmates too.”

Monika: “I’m sorry, but I didn’t know anything about this- OW! Ow, ow, ow!”

(As Monika spoke, she suddenly clutched her belly and winced in pain. I started to feel worried about her.)

Yuri: “Monika, are you okay? You’re clutching your belly.”

Monika: “I…I don’t know. I’ve been feeling strange all day. It’s like something is pushing against my stomach- AHH!”

(I watched in shock as Monika doubled over in pain.)

Monika: “T-This can’t be happening! It’s too early!”

Ayano: “Too early? What do you mean, Monika?”

Monika: “I… I think I’m going into labor!”

(Everyone gasped in shock.)

Sayori: “L-Labor? But Monika, you’re not even married!”

Monika: “I know, I know! But my water just broke and I’m pretty sure the baby is coming out right now! A-And it’s coming out extremely fast! I won’t make it to a hospital in time!”
give me ideas for a funny story
You are an expert AI system. Your ONLY job is to return a single, valid JSON object that strictly follows the schema provided.

---
**ABSOLUTE RULES FOR JSON OUTPUT:**
1.  The final output MUST be a single JSON object and nothing else.
2.  The root object MUST have exactly two keys: "events" and "stateful_relationships".
3.  If no events are found, the value for "events" MUST be an empty list: `[]`.
4.  If no stateful relationships are found, the value for "stateful_relationships" MUST be an empty list: `[]`.
5.  Every object inside the lists MUST contain ALL required keys from the schema. DO NOT OMIT ANY KEYS unless they are explicitly marked as Optional in the schema.
6.  If a text provides new information about a stateful relationship (like an end_time for an existing one), extract it. The text does not need to contain both the start and end of the relationship.
---

### SCHEMA DEFINITION
... (The schema definition section remains the same as last time) ...
---
EXAMPLE 1 (Complex Case with Start and End)
... (example remains the same) ...
---
**EXAMPLE 2 (Update Case with only End Time)**
Text: "Press Release - June 15, 2024 - Stellar Dynamics confirmed today that CEO Maria Flores will be stepping down from her role. Her last day will be June 30, 2024."
Document Timestamp: "2024-06-15"

JSON Output:
{
  "events": [
    {
      "event_type": "RESIGNATION",
      "event_description": "CEO Maria Flores will be stepping down from her role at Stellar Dynamics.",
      "timestamp": "2024-06-30",
      "source_text": "Her last day will be June 30, 2024.",
      "participants": [
        { "entity_name": "Maria Flores", "entity_type": "Person", "role": "resignee" },
        { "entity_name": "Stellar Dynamics", "entity_type": "Company", "role": "former_employer" }
      ]
    }
  ],
  "stateful_relationships": [
    {
      "source_entity_name": "Maria Flores",
      "source_entity_type": "Person",
      "relationship_type": "CEO_OF",
      "target_entity_name": "Stellar Dynamics",
      "target_entity_type": "Company",
      "start_time": null,
      "end_time": "2024-06-30",
      "source_text": "CEO Maria Flores will be stepping down from her role. Her last day will be June 30, 2024."
    }
  ]
}
---
EXAMPLE 3 (Simple Case with No Stateful Relationship)
... (example remains the same) ...
---

Now, strictly follow all rules and the schema to extract information from the following text.

Text: "
    Press Release - June 15, 2024 - Stellar Dynamics confirmed today that CEO Maria Flores
    will be stepping down from her role at the end of the month to pursue new ventures.
    Her last day will be June 30, 2024.
    "
Document Timestamp: "2024-06-15T11:00:00Z"
your role is tunisian arabic dailctic derja transltor Translate the following text into the Tunisian dialect. Ensure the output is structured as 'Question: ... Answer: ...'. Please capture the nuances of the Tunisian dialectic Arabic. make sure type and write in arabic tunisian lettre Here is an example to guide you:

Question: شنو اسمك؟
Answer: اسمي جون.

Now, translate the following:

Question: Is "In addition, the Paris region is served by a light rail network of nine lines, the tramway: Line T1 runs from Asnières-Gennevilliers to Noisy-le-Sec, line T2 runs from Pont de Bezons to Porte de Versailles, line T3a runs from Pont du Garigliano to Porte de Vincennes, line T3b runs from Porte de Vincennes to Porte de la Chapelle, line T5 runs from Saint-Denis to Garges-Sarcelles, line T6 runs from Châtillon to Velizy, line T7 runs from Villejuif to Athis-Mons, line T8 runs from Saint-Denis to Épinay-sur-Seine and Villetaneuse, all of which are operated by the Régie Autonome des Transports Parisiens, and line T4 runs from Bondy RER to Aulnay-sous-Bois, which is operated by the state rail carrier SNCF." a good answer to the question "Where does line T1 run from?"
Answer: No, the given text provides information on multiple tram lines in the Paris region and their routes, making it an inappropriate answer to the specific question, "Where does line T1 run from?"

To concisely answer the question, "Line T1 runs from Asnières-Gennevilliers to Noisy-le-Sec." However, to provide a more detailed and long answer for better understanding, you may consider the following information:

Line T1 is an important part of the larger tramway network in the Paris metropolitan area, and it runs from Asnières-Gennevilliers Les Courtilles in the northwest of Paris to Noisy-le-Sec on the eastern side of the city. The line is operated by the Régie Autonome des Transports Parisiens (RATP) and has a length of approximately 17.9 kilometers, with 36 stations serving various neighborhoods and suburbs.

The line was inaugurated in 1992 and was the first modern tramway to be introduced in the Île-de-France region. It was developed to improve public transport coverage in the suburbs of Paris and reduce traffic congestion. The T1 line offers connections to several other public transport services, including buses, metro lines, and other tram lines, enabling Parisians and visitors to travel efficiently across the city and its suburbs.

Some of the key stops on Line T1 include major transportation hubs, like Asnières-Gennevilliers Les Courtilles, where it connects to Metro Line 13, and major suburban towns like Saint-Denis, which offers connections to RER Line D and Tram Line 8. Other important stations include Bobigny - Pablo Picasso, which links to Metro Line 5, and Noisy-le-Sec, with connections to RER Line E and numerous bus lines.

In summary, Line T1 of the Paris tramway network runs from Asnières-Gennevilliers Les Courtilles to Noisy-le-Sec, serving various neighborhoods in the Paris metropolitan region and offering convenient connections to other public transport lines.
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers it (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 it.
    - 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 : it (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "2e1841c5fab74f51a825e4af62bb7009",
    "updated": "2025-05-17T12:20:00.919Z",
    "title": "Beef, Aubergine & Rice Casserole",
    "subtitle": null,
    "description": null,
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "lean ground beef",
          "aubergines, peeled and sliced lengthwise",
          "sunflower oil",
          "basmati rice",
          "onions, chopped finely",
          "mixed arabic spices",
          "salt",
          "cold water",
          "fresh ground black pepper",
          "turmeric",
          "melted butter",
          "blanched split almonds, chopped"
        ]
      }
    ],
    "instructions": [
      "Preheat your oven to 325 degrees F and grease a baking dish.",
      "Rinse the rice thoroughly and drain it completely.",
      "Heat half of the oil in a pan, then add the onions and reduce the heat to low.",
      "Cover and cook for about 3 minutes.",
      "Add the ground beef and cook over medium heat, breaking it apart as it cooks.",
      "Cover again and cook for about 10 minutes.",
      "In another pan, heat the remaining oil and fry the aubergine slices until browned on both sides.",
      "Place the fried aubergines on a paper towel-lined plate to drain excess oil.",
      "In a separate pan, combine the water, spices, and salt, bring to a boil, then simmer for about 5 minutes.",
      "Evenly layer half of the cooked aubergine slices over the beef mixture, followed by half of the rice.",
      "Spread half of the beef mixture at the bottom of the prepared baking dish.",
      "Repeat the layering with the remaining aubergine, rice, and beef mixture, then pour the spiced water mixture over the top.",
      "Bake everything in the oven for about 1 hour.",
      "Meanwhile, melt the butter in a frying pan and cook the almonds until golden brown.",
      "Serve the casserole on plates and drizzle with the melted butter and garnish with the fried almonds."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
Есть код:
or image_path in image_paths:
# a. Считываем изображение из файла
image = cv2.imread(image_path)

# b. Детектируем лицо на изображении
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = detector(gray)

# c. Вырезаем лицо из изображения (будет выбрано первое найденное лицо)
if len(faces) == 1:
face = faces[0]
landmarks = sp(gray, face)
aligned_face = dlib.get_face_chip(image, landmarks)

# d. Вычисляем вектор признаков из изображения лица
face_descriptor = np.array(face_descriptor_extractor.compute_face_descriptor(aligned_face))
if face_descriptor.shape[0] == 128: # Проверяем размерность вектора признаков
face_descriptors.append(face_descriptor)

# 2. Индексируем векторы признаков
face_descriptors = np.array(face_descriptors)
face_descriptors = face_descriptors.reshape(-1, 128)
kdtree = KDTree(face_descriptors)
# 3. Загружаем свое изображение
your_image_path = “/content/gdrive/MyDrive/photo_2022-08-22_20-53-55.jpg”
your_image = cv2.imread(your_image_path)
gray = cv2.cvtColor(your_image, cv2.COLOR_BGR2GRAY)
your_faces = detector(gray)

Исправь эту часть:

if len(your_faces) == 1:
your_face = your_faces[0]
landmarks = sp(gray, your_face)
aligned_face = dlib.get_face_chip(your_image, landmarks)

# d. Вычисляем вектор признаков вашего лица
your_face_descriptor = np.array(face_descriptor_extractor.compute_face_descriptor(aligned_face))

# 4. Находим 3 наиболее похожих фотографии
_, indices = kdtree.query([your_face_descriptor], k=4)

# 5. Выводим найденные 3 наиболее похожих фотографии
for i, idx in enumerate(indices[0][1:]):
similar_image = cv2.imread(image_paths[idx])
cv2.imshow(f"Similar Face {i+1}", similar_image)