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app.py

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+from transformers import pipeline
+import json
+from flask import Flask
+from flask import request
+import base64
+import io
+
+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")

requirements.txt

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+torch==2.0.1
+Pillow==9.5.0
+transformers==4.29.2
+Flask==2.0.3