create app file

app.py

@@ -0,0 +1,77 @@
+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
+
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = 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):
+    #Get image caption from Bip2 Gradio space
+    img_caption = get_caption(image_in)
+    print(f"Image caption from Blip2 Gradio Space 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;}
+                        """) as demo:
+  with gr.Column(scale=2):
+    pass
+  with gr.Column(scale=1):
+    label_top = gr.HTML(value= "<center>🖼️Upload an Image for your search📷</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")
+    #b1 = gr.Button("Search").style(full_width=False)
+    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")
+    pai_prompt = gr.Textbox(label="Image prompt from PlaygroundAI dataset", elem_id="pai_prompt")
+  with gr.Column(scale=2):
+    pass
+  
+  image_in.change(Image_similarity_search, image_in, [pai_prompt, img_search] )
+  #b1.click(Image_similarity_search, image_in, [pai_prompt, img_search] ) 
+
+demo.launch(debug=True)