code: from flask import Flask, request, jsonify, Response
  from flask_cors import CORS
  app = Flask(__name__)
  CORS(app)
  import argparse
  import io
  import PIL
  import duckdb
  import lancedb
  import lance
  import pyarrow.compute as pc
  from transformers import CLIPModel, CLIPProcessor, CLIPTokenizerFast
  import gradio as gr
  @app.route("/api/python", methods=['POST'])
  def get_image_blob():
      # Assuming you have the setup_clip_model, get_table, and embed_func functions
      data = request.get_json()  # Parse incoming JSON data
      prompt = data.get('prompt', '')  # Get the 'prompt' property from the JSON data
      tbl = get_table()
      print(data)
      emb = embed_func(prompt)
      print(emb)
      result_df = tbl.search(emb).limit(1).to_df()
  
      if not result_df.empty:
          image_col = "image"
          image, prompt = _extract(result_df)
          
          # Convert PIL.Image to bytes
          image_bytes = io.BytesIO()
          image.save(image_bytes, format='PNG')
          image_blob = image_bytes.getvalue()
  
          return Response(image_blob, content_type='image/png')
  
      return jsonify({"error": "No matching data found"})
  
  def _extract(df):
      image_col = "image"
      return PIL.Image.open(io.BytesIO(df.iloc[0][image_col])), df.iloc[0]["prompt"]
  
  
  MODEL_ID = None
  MODEL = None
  TOKENIZER = None
  PROCESSOR = None
  import pyarrow.compute as pc
  
  def get_table():
      db = lancedb.connect("data/lancedb")
      if "diffusiondb" in db.table_names():
          tbl= db.open_table("diffusiondb")
      else:
          # First data processing and full-text-search index
          data = lance.dataset("rawdata.lance").to_table()
          # remove null prompts
          tbl = db.create_table("diffusiondb", data.filter(~pc.field("prompt").is_null()), mode="overwrite")
          tbl = tbl.create_fts_index(["prompt"])
      return tbl
  def setup_clip_model():
      global MODEL_ID, MODEL, TOKENIZER, PROCESSOR
      MODEL_ID = "openai/clip-vit-base-patch32"
      TOKENIZER = CLIPTokenizerFast.from_pretrained(MODEL_ID)
      MODEL = CLIPModel.from_pretrained(MODEL_ID)
      PROCESSOR = CLIPProcessor.from_pretrained(MODEL_ID)
  
  def embed_func(query):
      inputs = TOKENIZER([query], padding=True, return_tensors="pt")
      text_features = MODEL.get_text_features(**inputs)
      return text_features.detach().numpy()[0]
  
  setup_clip_model()
  
apis: lancedb.connect
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