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)