Added a new endpoint to compute closest positive and negative values …

app.py

@@ -38,6 +38,8 @@
 import json
 import os
 import pickle
+import numpy as np
+from functools import lru_cache
 
 app = Flask(__name__)
 
@@ -57,6 +59,41 @@
 app.register_blueprint(cfg.SWAGGER_BLUEPRINT, url_prefix = cfg.SWAGGER_URL)
 FRONT_LOG_FILE = 'front_log.json'
 
+@lru_cache(maxsize=1)
+def get_values_embedding_function():
+    """
+    Getting the embedding function for the /values endpoint.
+    Cached to avoid reloading the model multiple times.
+
+    Returns:
+        Embedding function callable
+    """
+    model_id, model_path = save_model.save_model()
+    return recommendation_handler.get_embedding_func(inference='local', model_id=model_path)
+
+@lru_cache(maxsize=1)
+def get_values_centroids():
+    """
+    Getting the positive and negative value centroids for the /values endpoint.
+    Cached to avoid reloading the data multiple times.
+
+    Returns:
+        Dictionary with 'positive' and 'negative' centroid embeddings
+    """
+    prompt_json = recommendation_handler.populate_json()
+    positive_category_centroid = {}
+    negative_category_centroid = {}
+
+    for category in prompt_json['positive_values']:
+        positive_category_centroid[category['label']] = np.array(category['centroid'])
+
+    for category in prompt_json['negative_values']:
+        negative_category_centroid[category['label']] = np.array(category['centroid'])
+
+    return {
+        'positive': positive_category_centroid,
+        'negative': negative_category_centroid
+    }
 
 @app.route("/")
 def index():
@@ -109,7 +146,7 @@ def get_thresholds():
 @cross_origin()
 def recommend_local():
     model_id, _ = save_model.save_model()
-    prompt_json, _ = recommendation_handler.populate_json()
+    prompt_json = recommendation_handler.populate_json()
     args = request.args
     print("args list = ", args)
     prompt = args.get("prompt")
@@ -166,6 +203,42 @@ def demo_inference():
         return response
     except:
         return "Model Inference failed.", 500
+
+@app.route("/values", methods=['GET'])
+@cross_origin()
+def get_values():
+    """
+    Getting positive and negative values for a given prompt using cached embedding function and centroids for performance.
+    """
+    args = request.args
+    prompt = args.get("prompt")
+
+    # validating input
+    if not prompt:
+        return jsonify({"error": "Missing required parameter: prompt"}), 400
+
+    if not isinstance(prompt, str):
+        return jsonify({"error": "Parameter 'prompt' must be a string"}), 400
+
+    if len(prompt.strip()) == 0:
+        return jsonify({"error": "Parameter 'prompt' cannot be empty"}), 400
+
+    try:
+        embedding_fn = get_values_embedding_function()
+        centroids = get_values_centroids()
+
+        values = recommendation_handler.get_values(
+            prompt, 
+            centroids['positive'], 
+            centroids['negative'],
+            embedding_fn
+        )
+
+        return jsonify(values)
+
+    except Exception as e:
+        logger.error(f'Error in /values endpoint: {str(e)}')
+        return jsonify({"error": "Internal server error processing prompt"}), 500
 
 if __name__=='__main__':
     debug_mode = os.getenv('FLASK_DEBUG', 'False').lower() in ['true', '1', 't']

control/recommendation_handler.py

@@ -335,3 +335,82 @@ def get_thresholds(
     thresholds['remove_higher_threshold'] = round(remove_similarities_df.describe([.9]).loc['90%', 'similarity'], 1)
 
     return thresholds
+
+def get_values(
+    prompt,
+    positive_embeddings,
+    negative_embeddings,
+    embedding_fn = None
+):
+    """
+    Compute positive and negative value associations for each sentence in the input prompt.
+
+    Args:
+        prompt: Input prompt text.
+        positive_embeddings: Dictionary mapping positive value labels to centroid embeddings.
+        negative_embeddings: Dictionary mapping negative value labels to centroid embeddings.
+        embedding_fn: Function to generate embeddings from text.
+
+    Returns:
+        Dictionary containing sentences with their associated positive and negative values and similarity scores.
+    """
+
+    if embedding_fn is None:
+        # using all-MiniLM-L6-v2 locally by default
+        embedding_fn = get_embedding_func('local', model_id='sentence-transformers/all-MiniLM-L6-v2')
+
+    sentences = split_into_sentences(prompt)
+
+    # bifurcating and filtering out empty sentences
+    sentences = [s for s in sentences if s.strip()]
+
+    values = {}
+    values["prompts"] = []
+
+    # returning if no valid sentences
+    if not sentences:
+        return values
+
+    # generating all sentence embeddings in a single call by batching all
+    sentence_embeddings = embedding_fn(sentences)
+    sentence_embeddings = np.array(sentence_embeddings)
+
+    # ensuring embeddings have correct shape - expanding embeddings of all sentences
+    if len(sentence_embeddings.shape) == 1:
+        sentence_embeddings = np.expand_dims(sentence_embeddings, axis=0)
+
+    # processing each sentence with its corresponding embedding
+    for idx, sentence in enumerate(sentences):
+
+        sentence_embedding = sentence_embeddings[idx]
+
+        max_similarity_positive = -1
+        positive_label = None
+        for label, centroid in positive_embeddings.items():
+            similarity = cosine_similarity(
+                np.expand_dims(sentence_embedding, axis=0),
+                np.array([centroid])
+            )[0, 0]
+            if similarity > max_similarity_positive:
+                max_similarity_positive = similarity
+                positive_label = label
+
+        max_similarity_negative = -1
+        negative_label = None
+        for label, centroid in negative_embeddings.items():
+            similarity = cosine_similarity(
+                np.expand_dims(sentence_embedding, axis=0),
+                np.array([centroid])
+            )[0, 0]
+            if similarity > max_similarity_negative:
+                max_similarity_negative = similarity
+                negative_label = label
+
+        values["prompts"].append({
+            "sentence": sentence,
+            "positive_value": {"label": positive_label, "similarity": float(max_similarity_positive)},
+            "negative_value": {"label": negative_label, "similarity": float(max_similarity_negative)}
+        })
+
+    return values
+