import os 
from newsapi import NewsApiClient
from gradio_client import Client

HF_TOKEN = os.getenv("HF_TOKEN")
NEWSAPI = os.getenv("NEWSAPI")

# example input: prompt = "Beautiful Sky with "Gradio is love" written over it"
# defining a function to generate music using Gradio demo of TextDiffusers hosted on Spaces
def generate_image(prompt):
  """
  generate an image based on the prompt provided
  """
  client = Client("https://jingyechen22-textdiffuser.hf.space/")
  result = client.predict(
          prompt,	# str  in 'Input your prompt here. Please enclose keywords with 'single quotes', you may refer to the examples below. The current version only supports input in English characters.' Textbox component
          20,	# int | float (numeric value between 1 and 50) in 'Sampling step' Slider component
          7.5,	# int | float (numeric value between 1 and 9) in 'Scale of classifier-free guidance' Slider component
          1,	# int | float (numeric value between 1 and 4) in 'Batch size' Slider component
          "Stable Diffusion v2.1",	# str  in 'Pre-trained Model' Radio component
          fn_index=1)
  return result[0]

# example input: input_text = "A cheerful country song with acoustic guitars"
# defining a function to generate music using Gradio demo of MusicGen hosted on Spaces
#input melody example = "/content/bolero_ravel.mp3"
def generate_music(input_text, input_melody ):
  """
  generate music based on an input text
  """
  client = Client("https://ysharma-musicgendupe.hf.space/", hf_token=HF_TOKEN)
  result = client.predict(
          "melody",	# str  in 'Model' Radio component
          input_text,	# str  in 'Input Text' Textbox component
          input_melody, 	# str (filepath or URL to file) in 'Melody Condition (optional)' Audio component
          5,	# int | float (numeric value between 1 and 120) in 'Duration' Slider component
          250,	# int | float  in 'Top-k' Number component
          0,	# int | float  in 'Top-p' Number component
          1,	# int | float  in 'Temperature' Number component
          3,	# int | float  in 'Classifier Free Guidance' Number component
          fn_index=1)
  return result



generate_music_func = {
    "name": "generate_music",
    "description": "generate music based on an input text and input melody",
    "parameters": {
        "type": "object",
        "properties": {
            "input_text": {
                "type": "string",
                "description": "input text for the music generation"
            },
            "input_melody": {
                "type": "string",
                "description": "file path of input melody for the music generation"
            }
        },
        "required": ["input_text", "input_melody"]
    }
}

# example input: input_image = "cat.jpg"
# defining a function to generate caption using a image caption Gradio demo hosted on Spaces
def generate_caption(input_image ):
  """
  generate caption for the input image
  """
  client = Client("https://nielsr-comparing-captioning-models.hf.space/")
  temp = input_image.split('/')
  if len(temp) == 1:
    input_image = temp[0]
  else:
    input_image = temp[-1]
  result = client.predict(
          input_image,	
          api_name="/predict")
  result = "The image can have any one of the following captions, all captions are correct: " + ", or ".join([f"'{caption.replace('.','')}'" for caption in result])
  return result


generate_caption_func = {
    "name": "generate_caption",
    "description": "generate caption for the image present at the filepath provided",
    "parameters": {
        "type": "object",
        "properties": {
            "input_image": {
                "type": "string",
                "description": "filepath for the input image"
            },
        },
        "required": ["input_image"]
    }
}


generate_image_func = {
    "name": "generate_image",
    "description": "generate image based on the input text prompt",
    "parameters": {
        "type": "object",
        "properties": {
            "prompt": {
                "type": "string",
                "description": "input text prompt for the image generation"
            }
        },
        "required": ["prompt"]
    }
}


# defining a function to get the most relevant world news for a given query
# example query: Joe Biden presidency
def get_news(search_query):
    """
    get top three news items for your search query
    """
    newsapi = NewsApiClient(api_key=NEWSAPI)
    docs = newsapi.get_everything(q=search_query,
                        language='en',
                        sort_by = 'relevancy',
                        page_size=3,
                        page=1
                        )['articles']
    res = [news['description'] for news in docs]
    res = [item.replace('<li>','').replace('</li>','').replace('<ol>','') for item in res]
    res = "\n".join([f"{i}.{ res[i-1]}" for i in range(1,len(res)+1)])
    return "Following list has the top three news items for the given search query : \n" + res


get_news_func = {
    "name": "get_news",
    "description": "get top three engilsh news items for a given query, sorted by relevancy",
    "parameters": {
        "type": "object",
        "properties": {
            "search_query": {
                "type": "string",
                "description": "input search string to search for relevant news"
            },
        },
        "required": ["search_query"]
    }
}


#dict_plugin_functions = { 'generate_music_func':{'dict': generate_music_func , 'func': generate_music}, 
#                         'generate_image_func':{'dict':generate_image_func, 'func':generate_image} }

#dict_plugin_functions = { 'generate_music_func':{'dict': generate_music_func , 'func': generate_music}, 
#                         'generate_image_func':{'dict':generate_image_func, 'func':generate_image},
#                          'generate_caption_func' : {'dict':generate_caption_func, 'func':generate_caption}
#                          }

dict_plugin_functions = { 'generate_music_func':{'dict': generate_music_func , 'func': generate_music}, 
                         'generate_image_func':{'dict':generate_image_func, 'func':generate_image},
                          'generate_caption_func' : {'dict':generate_caption_func, 'func':generate_caption},
                          'get_news_func' : {'dict':get_news_func, 'func':get_news}
                          }