app.py

from transformers import pipeline
import json
from flask import Flask
from flask import request
import base64
import io
import os

os.environ['SENTENCE_TRANSFORMERS_HOME'] = '/code/.cache'
os.environ['TRANSFORMERS_CACHE '] = '/code/.cache'

app = Flask(__name__)


detector = pipeline("zero-shot-object-detection", model="google/owlvit-base-patch32",device=-1,framework="pt")
#depth_estimator = pipeline("depth-estimation", model="Intel/dpt-large")


def process(image, items):
    preds = detector("https://visualization.graberblinds.com/assets/sample_sessions/02e1d080-c4bf-4cdc-b1bc-f39f9b2a2230_thumb.jpg", candidate_labels=items)
    return preds



@app.route("/",methods=["POST"])
def detect():
    
        from PIL import Image
        body=request.get_json()
        base64_str=body['img']
        # Assuming base64_str is the string value without 'data:image/jpeg;base64,'
        img = Image.open(io.BytesIO(base64.decodebytes(bytes(base64_str, "utf-8"))))
        items ="window"
        preds=process(img,items)
        response=app.make_response(json.dumps(preds))
        response.content_type="application/json"
        return response
  

app.run(debug=True,port="7860")