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+# USE THE OFFICIAL PYTHON 3.9 SLIM IMAGE
+FROM python:3.9-slim
+
+# SET THE WORKING DIRECTORY INSIDE THE CONTAINER
+WORKDIR /code
+
+# COPY THE REQUIREMENTS FILE FIRST FOR EFFICIENT CACHING
+COPY ./requirements.txt /code/requirements.txt
+
+# INSTALL ALL THE REQUIRED LIBRARIES
+RUN pip install --no-cache-dir --upgrade pip -r /code/requirements.txt
+
+# COPY THE REST OF YOUR APPLICATION CODE
+COPY ./app.py /code/app.py
+COPY ./model_handler.py /code/model_handler.py
+
+# HUGGING FACE SPACES RUNS APPS ON PORT 7860
+EXPOSE 7860
+
+# THE COMMAND TO START THE GUNICORN PRODUCTION SERVER
+# IT RUNS THE 'FLASK_APP' OBJECT FROM THE 'app.py' FILE
+CMD ["gunicorn", "--bind", "0.0.0.0:7860", "app:FLASK_APP"]

app.py

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+from flask import Flask, request, jsonify
+from model_handler import MODEL_HANDLER
+
+# INITIALIZING THE FLASK APP
+FLASK_APP = Flask(__name__)
+
+# --- API ENDPOINT DEFINITIONS ---
+
+@FLASK_APP.route("/", methods=["GET"])
+def health_Check():
+    # HEALTH CHECKING ENDPOINT TO CONFIRM THE API IS LIVE
+    return jsonify({
+        "status": "ok",
+        "message": "Semantic Similarity API is operational."
+    })
+
+@FLASK_APP.route("/predict", methods=["POST"])
+def predict_Handler():
+    """
+    HANDLES THE PREDICTION REQUEST.
+    """
+    # GETTING THE JSON PAYLOAD FROM THE REQUEST
+    input_Payload = request.get_json()
+
+    # VALIDATING THE INCOMING DATA
+    if not input_Payload:
+        return jsonify({"error": "BAD REQUEST: Missing JSON body."}), 400
+    
+    text1_Value = input_Payload.get("text1")
+    text2_Value = input_Payload.get("text2")
+
+    if not text1_Value or not text2_Value:
+        return jsonify({"error": "BAD REQUEST: 'text1' and 'text2' are required fields."}), 400
+
+    # PROCESSING THE REQUEST AND GET THE SCORE
+    try:
+        # USING THE MODEL HANDLER TO GET THE SIMILARITY SCORE
+        similarityScore = MODEL_HANDLER.calculate_Similarity(text1_Value, text2_Value)
+
+        # THE RESPONSE BODY 
+        response_Body = {
+            "similarity_score": similarityScore
+        }
+        
+        return jsonify(response_Body), 200
+
+    except Exception as e:
+        # CATCHING ANY UNEXPECTED ERRORS
+        print(f"INTERNAL ERROR: {e}")
+        return jsonify({"error": "An internal server error occurred."}), 500
+
+if __name__ == "__main__":
+    # THIS PART IS ONLY FOR LOCAL TESTING. GUNICORN WILL RUN THE APP ON HUGGING FACE
+    FLASK_APP.run(host="0.0.0.0", port=8000, debug=True)

model_handler.py

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+from sentence_transformers.cross_encoder import CrossEncoder
+import torch
+
+class SimilarityModelHandler:
+    # HOLDING THE MODEL INSTANCE TO PREVENT RELOADING
+    SIMILARITY_MODEL_INSTANCE = None
+
+    def __init__(self):
+        # CONSTRUCTOR: LOADING THE MODEL IF IT DOESN'T EXIST
+        if not SimilarityModelHandler.SIMILARITY_MODEL_INSTANCE:
+            print("INITIALIZING AND LOADING THE MODEL...")
+            # CHECKING FOR GPU, FALLBACK TO CPU
+            device = 'cuda' if torch.cuda.is_available() else 'cpu'
+            print(f"SERVICE IS RUNNING ON DEVICE: {device}")
+            
+            # LOADING THE PRE-TRAINED CROSS-ENCODER MODEL
+            model_Name = 'cross-encoder/stsb-roberta-large'
+            SimilarityModelHandler.SIMILARITY_MODEL_INSTANCE = CrossEncoder(model_Name, device=device)
+            print("MODEL LOADED SUCCESSFULLY.")
+
+    def calculate_Similarity(self, text_One: str, text_Two: str) -> float:
+        """
+        CALCULATES THE SIMILARITY SCORE BETWEEN TWO TEXTS.
+        """
+        # GETTING THE SCORE FROM THE MODEL( 0-1 )
+        finalScore = self.SIMILARITY_MODEL_INSTANCE.predict([(text_One, text_Two)])
+
+        # CONVERTING FROM NUMPY ARRAY TO A SIMPLE FLOAT
+        return finalScore.item()
+
+
+# CREATING A SINGLE INSTANCE TO BE USED BY THE API
+MODEL_HANDLER = SimilarityModelHandler()

requirements.txt

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+# Libraries for the model
+sentence-transformers>=2.2.0
+torch>=1.9.0
+
+# Libraries for the API server
+Flask>=2.0.0
+gunicorn>=20.0.0