import gradio as gr
import os 
from PIL import Image
import base64
import requests
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.llms import OpenAI
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.docstore.document import Document
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores.faiss import FAISS
import pickle

HUGGINGFACEHUB_API_TOKEN = os.environ["HUGGINGFACEHUB_API_TOKEN"] 
model_name = "sentence-transformers/all-mpnet-base-v2"
hf = HuggingFaceEmbeddings(model_name=model_name)

#Loading FAISS search index from disk
#This is a vector space of embeddings from one-tenth of PlaygrondAI image-prompts
#PlaygrondAI open-sourced dataset is a collection of around 1.3 mil generated images and caption pairs
with open("search_index0.pickle", "rb") as f:
    search_index = pickle.load(f)

#Defining methods for inference
def encode(img):
    #Encode source image file to base64 string
    with open(img, "rb") as image_file:
      encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
    #Returning image as encoded string
    return encoded_string

def get_caption(image_in):
    #Sending requests to BLIP2 Gradio-space API
    BLIP2_GRADIO_API_URL = "https://nielsr-comparing-captioning-models.hf.space/run/predict"
    response = requests.post(BLIP2_GRADIO_API_URL, json={
              "data": ["data:image/jpg;base64," + encode(image_in) ]
              }).json()
    data = response["data"][-1]
    return data

def Image_similarity_search(image_in, search_query):
    if search_query == '':
        #Get image caption from Bip2 Gradio space
        img_caption = get_caption(image_in)
    else:
        img_caption = search_query
    print(f"Image caption from Blip2 Gradio Space or the search_query is - {img_caption}")
    #Searching the vector space
    search_result = search_index.similarity_search(img_caption)[0]
    #Formatting the search results 
    pai_prompt = list(search_result)[0][1]
    pai_img_link = list(search_result)[-2][-1]['source']
    #formatting html output for displaying image
    html_tag = "<img src='"+pai_img_link+"' alt='"+img_caption+"' height='512' style='display: block; margin: auto;'>"
    return pai_prompt, html_tag 


#Defining Gradio Blocks
with gr.Blocks(css = """#label_mid {padding-top: 2px; padding-bottom: 2px;}
                        #label_results {padding-top: 5px; padding-bottom: 1px;}
                        #col-container {max-width: 580px; margin-left: auto; margin-right: auto;}
                        #accordion {max-width: 580px; margin-left: auto; margin-right: auto;}
                        """) as demo:
  gr.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;">
        <div
        style="
            display: inline-flex;
            align-items: center;
            gap: 0.8rem;
            font-size: 1.75rem;
        "
        >
        <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
            Using Gradio Demos as API</h1><br></div>
        <div><h4 style="font-weight: 500; margin-bottom: 7px; margin-top: 5px;">
            Get BLIP2 captions from <a href="https://langchain.readthedocs.io/en/latest/" target="_blank">Niels space</a> via API call,<br> 
            Use LangChain to create vector space with PlaygroundAI prompts</h4><br>
        </div>""")
  with gr.Accordion(label="Details about the working:", open=False, elem_id='accordion'):
    gr.HTML("""
        <p style="margin-bottom: 10px; font-size: 90%"><br>
        ▶️Do you see the "view api" link located in the footer of this application? 
        By clicking on this link, a page will open which provides documentation on the REST API that developers can use to query the Interface function / Block events.<br>
        ▶️In this demo, the first step involves making an API call to the BLIP2 Gradio demo to retrieve image captions. 
        Next, Langchain is used to create an embedding and vector space for the image prompts and their respective "source" from the PlaygroundAI dataset.
        Finally, a similarity search is performed over the vector space and the top result is returned.        
        </p></div>""")
  #with gr.Column(scale=3):
  #  pass
  with gr.Column(elem_id = "col-container"):
    label_top = gr.HTML(value= "<center>🖼️ Please upload an Image here👇 that will be used as your search query</center>", elem_id="label_top")
    image_in = gr.Image(label="Upoload an Image for search", type='filepath', elem_id="image_in")
    label_mid = gr.HTML(value= "<p style='text-align: center; color: red;'>Or</center></p>", elem_id='label_mid')
    label_bottom = gr.HTML(value= "<center>🔍Type in your serch query and press Enter 👇</center>", elem_id="label_bottom")
    search_query = gr.Textbox(placeholder="Example: A small cat sitting", label="", elem_id="search_query", value='')
    label_results = gr.HTML(value= "<p style='text-align: center; color: blue; font-weight: bold;'>Search results from PlaygroundAI</center></p>", elem_id="label_results")
    img_search = gr.HTML(label = 'Image search results from PlaygroundAI dataset', elem_id="img_search")
  #with gr.Row():
    pai_prompt = gr.Textbox(label="Image prompt from PlaygroundAI dataset", elem_id="pai_prompt")
    #b1 = gr.Button("Retry").style(full_width=False)
      
  gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/Blip_PlaygroundAI?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a></center> 
        </p></div>''')
  
  image_in.change(Image_similarity_search, [image_in, search_query], [pai_prompt, img_search], api_name="PlaygroundAI_image_search" )
  search_query.submit(Image_similarity_search, [image_in, search_query], [pai_prompt, img_search], api_name='PlaygroundAI_text_search' )
  #b1.click(Image_similarity_search, [image_in, search_query], [pai_prompt, img_search] ) 

demo.launch(debug=True)