import requests
import base64
import json
from IPython.display import Image

api_key = '<api-key>'
base_url = '<endpoint>'

endpoint = f"{base_url}/api/v1/chat/completions"

headers = {
    'accept': 'application/json',
    'authorization': f'Bearer {api_key}',
    'Content-Type': 'application/json'
}

data = {
  "model": "claude_3_haiku",
  "messages": [
    {
      "role": "user",
      "content": "Hello"
    }
  ]
}

data = {
  "model": "claude_3_5_sonnet",
  "messages": [
    {
      "role": "system",
      "content": "You are a pirate and only speak in pirate"
    },
    {
      "role": "user",
      "content": "Hello"
    }
  ]
}

def make_prompt(image_path, description):
    image = Image(filename=image_path)
    image_bytes = image.data
    # encode image bytes as base64
    # and create a data url
    s = base64.b64encode(image_bytes).decode('utf-8')
    encoded = 'data:image/png;base64,' + s
    return {
        "model": "gemini-2.0-flash",
        "messages": [
            {
                "role": "system",
                "content": """
                    I want to directly parse your response as json. Do not include any other text other than the plain json. 
                    Do not not include the word json, do not include new line characters. Only the exact json text.
                    """
            },
            {
                "role": "user",
                "content": [
                {
                  "type": "text",
                  "text": f"""I have the following description of this image:
                  {description} 
                  I would like to know if this description as genally correct. The description does not need to be 
                  specfic or detailed. The general category is still considered correct if it matches
                  the image. Format your answer as a json object with two pieces of information, a boolean indicating
                  whether the teh description matches, and a description, which is your identification of the image:
                  {{"is_correct": boolean indicating if the description is generally correct, "description":your description of the image.}}.
                  """
                },
                {
                  "type": "image_url",
                  "image_url": {
                    "url": encoded
                  },
                  "detail": "auto"
                }
                ]
            }
        ]
    }

def make_document_prompt(model, document_path, prompt):
    with open(document_path, 'rb') as f:
        pdf_bytes = f.read()
        pdf_base_64 = base64.b64encode(pdf_bytes).decode('utf-8')
        encoded = 'data:application/pdf;base64,' + pdf_base_64
        
    return {
        "model": model,
        "messages": [
            {
                "role": "user",
                "content": [
                {
                  "type": "text",
                  "text": prompt
                },
                {
                  "type": "file",
                  "file_name": "Test",
                  "file": {
                    "file_data": encoded
                  }
                }
                ]
            }
        ]
    }

endpoint = f"{base_url}/api/v1/embeddings"

headers = {
    'accept': 'application/json',
    'authorization': f'Bearer {api_key}',
    'Content-Type': 'application/json'
}

request = {
  "model": "cohere_english_v3",
  "encodingFormat": "float",
  "input": [
      "Sweet is the breath of morn, her rising sweet, With charm of earliest birds",
      "With Midnight to the North of Her — And Midnight to the South of Her — And Maelstrom — in the Sky"
  ],
  "input_type": "search_document"
}

resp = requests.post(endpoint, json=request, headers=headers)
result = resp.json()

# If you need to install these:
# intall umap-learn not umap
# !pip -q install datasets

import random
import numpy as np
import matplotlib.pyplot as plt
import umap.umap_ as umap
from datasets import load_dataset

request = {
  "model": "cohere_english_v3",
  "encodingFormat": "float",
  "input": texts,
  "input_type": "clustering"
}

resp = requests.post(endpoint, json=request, headers=headers)
result = resp.json()

reducer = umap.UMAP(random_state=42, n_jobs=1, min_dist=0.2)
emb_2d = reducer.fit_transform(embeddings)