#!/usr/bin/env python
# coding: utf-8

# # Call and response: your first Bing Image Search query in Python
# 
# The Bing Image Search API provides an experience similar to Bing.com/Images by letting you send a user search query to Bing and get back a list of relevant images.
# 
# This walkthrough demonstrates a simple example of calling into the Bing Image Search API and post-processing the resulting JSON object. For more information, see [Bing Image Search documentation](https://docs.microsoft.com/en-us/rest/api/cognitiveservices/bing-images-api-v7-reference).
# 
# You can run this example as a Jupyter notebook on [MyBinder](https://mybinder.org) by clicking on the launch Binder badge: 
# 
# [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/Microsoft/cognitive-services-notebooks/master?filepath=BingImageSearchAPI.ipynb)
# 
# ## Prerequisites
# 
# You must have a [Cognitive Services API account](https://docs.microsoft.com/azure/cognitive-services/cognitive-services-apis-create-account) with **Bing Search APIs**. The [free trial](https://azure.microsoft.com/try/cognitive-services/?api=bing-web-search-api) is sufficient for this quickstart. You need the access key provided when you activate your free trial, or you may use a paid subscription key from your Azure dashboard.
# 
# ## Running the walkthrough
# To continue with the walkthrough, set `subscription_key` to your API key for the Bing API service.

# In[1]:


subscription_key = None
assert subscription_key


# Next, verify that the `search_url` endpoint is correct. At this writing, only one endpoint is used for Bing search APIs. If you encounter authorization errors, double-check this value against the Bing search endpoint in your Azure dashboard.

# In[2]:


search_url = "https://api.cognitive.microsoft.com/bing/v7.0/images/search"


# Set `search_term` to look for images of puppies.

# In[1]:


search_term = "puppies"


# The following block uses the `requests` library in Python to call out to the Bing search APIs and return the results as a JSON object. Observe that we pass in the API key via the `headers` dictionary and the search term via the `params` dictionary. To see the full list of options that can be used to filter search results, refer to the [REST API](https://docs.microsoft.com/en-us/rest/api/cognitiveservices/bing-images-api-v7-reference) documentation.

# In[4]:


import requests

headers = {"Ocp-Apim-Subscription-Key" : subscription_key}
params  = {"q": search_term, "license": "public", "imageType": "photo"}
response = requests.get(search_url, headers=headers, params=params)
response.raise_for_status()
search_results = response.json()


# The `search_results` object contains the actual images along with rich metadata such as related items. For example, the following line of code can extract the thumbnail URLS for the first 16 results.

# In[5]:


thumbnail_urls = [img["thumbnailUrl"] for img in search_results["value"][:16]]


# Then, we can use the `PIL` library to download the thumbnail images and the `matplotlib` library to render them on a $4 \times 4$ grid.

# In[ ]:


get_ipython().run_line_magic('matplotlib', 'inline')
import matplotlib.pyplot as plt
from PIL import Image
from io import BytesIO

f, axes = plt.subplots(4, 4)
for i in range(4):
    for j in range(4):
        image_data = requests.get(thumbnail_urls[i+4*j])
        image_data.raise_for_status()
        image = Image.open(BytesIO(image_data.content))        
        axes[i][j].imshow(image)
        axes[i][j].axis("off")


# ## Next steps
# 
# > [!div class="nextstepaction"]
# > [Bing Image Search single-page app tutorial](../tutorial-bing-image-search-single-page-app.md)
# 
# ## See also 
# 
# [Bing Image Search overview](../overview.md)  
# [Try it](https://azure.microsoft.com/services/cognitive-services/bing-image-search-api/)  
# [Get a free trial access key](https://azure.microsoft.com/try/cognitive-services/?api=bing-image-search-api)  
# [Bing Image Search API reference](https://docs.microsoft.com/rest/api/cognitiveservices/bing-images-api-v7-reference)