Update ontochat/functions.py

ontochat/functions.py

@@ -105,13 +105,6 @@ current_preidentified = [
     5. The language is concise, precise, and written in a professional tone.
     6. The final refined answer is structured as bullet points, with each outcome represented as a separate bullet.""",
     ]
-
-
-
-
-
-def load_example(selection): 
-    return current_preidentified[selection]
     
 def set_openai_api_key(api_key: str):
     global openai_api_key
@@ -186,70 +179,73 @@ def user_story_generator(message, history):
 
     return history, ""
 
-def cq_generator(message, history):
-    check_api_key()
-    instructions = [{
-        "role": "assistant",
-        "content": "You are a conversational ontology engineering assistant."
-    }, {
-        "role": "user",
-        "content": "Here are instructions for you on how to generate high-quality competency questions. First, here "
-                   "are some good examples of competency questions generated from example data. Who performs the song? "
-                   "from the data Yesterday was performed by Armando Rocca, When (what year) was the building built? "
-                   "from the data The Church was built in 1619, In which context is the building located? from the "
-                   "data The Church is located in a periurban context. Second, how to make them less complex. Take the "
-                   "generated competency questions and check if any of them can be divided into multiple questions. If "
-                   "they do, split the competency question into multiple competency questions. If it does not, leave "
-                   "the competency question as it is. For example, the competency question Who wrote The Hobbit and in "
-                   "what year was the book written? must be split into two competency questions: Who wrote the book? "
-                   "and In what year was the book written?. Another example is the competency question, When was the "
-                   "person born?. This competency question cannot be divided into multiple questions. Third, how to "
-                   "remove real entities to abstract them. Take the competency questions and check if they contain "
-                   "real-world entities, like Freddy Mercury or 1837. If they do, change those real-world entities "
-                   "from these competency questions to more general concepts. For example, the competency question "
-                   "Which is the author of Harry Potter? should be changed to Which is the author of the book?. "
-                   "Similarly, the competency question Who wrote the book in 2018? should be changed to Who wrote the "
-                   "book, and in what year was the book written?"
-    }]
-    messages = build_messages(history)
-    messages.append({
-        "role": "user",
-        "content": message
-    })
-    bot_message = chat_completion(openai_api_key, instructions + messages)
-    history.append([message, bot_message])
-    return bot_message, history, ""
-
-def load_example_user_story():
-    with open("data/Linka#1_MusicKnowledge.md", "r") as f:
-        return f.read()
-
-def clustering_generator(cqs, cluster_method, n_clusters):
-    check_api_key()
-    if n_clusters:
-        n_clusters = int(n_clusters)
-
-    cqs, cq_embeddings = compute_embeddings(cqs)
-
-    if cluster_method == "Agglomerative clustering":
-        cq_clusters, cluster_image = agglomerative_clustering(cqs, cq_embeddings, n_clusters)
-    else:  # cluster_method == "LLM clustering"
-        cq_clusters, cluster_image = llm_cq_clustering(cqs, n_clusters, openai_api_key)
-
-    return cluster_image, json.dumps(cq_clusters, indent=4)
-
-def ontology_testing(ontology_file, ontology_desc, cqs):
-    check_api_key()
-    verbalisation = verbalise_ontology(ontology_file, ontology_desc, "")
-    messages = [{
-        "role": "system",
-        "content": "Please (1) provide a description of the ontology uploaded to provide basic information and "
-                   "additional context, (2) give the competency questions (CQs) that you want to test with."
-    }, {
-        "role": "user",
-        "content": verbalisation + "\n" + f"Given the above ontology, please label each competency question: {cqs} to "
-                                          f"determine whether it is addressed properly or not. Format your response in"
-                                          f" ['yes': 'CQ1', 'no': 'CQ2', ...]."
-    }]
-    bot_message = chat_completion(openai_api_key, messages)
-    return bot_message
+def load_example(selection): 
+    return current_preidentified[selection]
+
+# def cq_generator(message, history):
+#     check_api_key()
+#     instructions = [{
+#         "role": "assistant",
+#         "content": "You are a conversational ontology engineering assistant."
+#     }, {
+#         "role": "user",
+#         "content": "Here are instructions for you on how to generate high-quality competency questions. First, here "
+#                    "are some good examples of competency questions generated from example data. Who performs the song? "
+#                    "from the data Yesterday was performed by Armando Rocca, When (what year) was the building built? "
+#                    "from the data The Church was built in 1619, In which context is the building located? from the "
+#                    "data The Church is located in a periurban context. Second, how to make them less complex. Take the "
+#                    "generated competency questions and check if any of them can be divided into multiple questions. If "
+#                    "they do, split the competency question into multiple competency questions. If it does not, leave "
+#                    "the competency question as it is. For example, the competency question Who wrote The Hobbit and in "
+#                    "what year was the book written? must be split into two competency questions: Who wrote the book? "
+#                    "and In what year was the book written?. Another example is the competency question, When was the "
+#                    "person born?. This competency question cannot be divided into multiple questions. Third, how to "
+#                    "remove real entities to abstract them. Take the competency questions and check if they contain "
+#                    "real-world entities, like Freddy Mercury or 1837. If they do, change those real-world entities "
+#                    "from these competency questions to more general concepts. For example, the competency question "
+#                    "Which is the author of Harry Potter? should be changed to Which is the author of the book?. "
+#                    "Similarly, the competency question Who wrote the book in 2018? should be changed to Who wrote the "
+#                    "book, and in what year was the book written?"
+#     }]
+#     messages = build_messages(history)
+#     messages.append({
+#         "role": "user",
+#         "content": message
+#     })
+#     bot_message = chat_completion(openai_api_key, instructions + messages)
+#     history.append([message, bot_message])
+#     return bot_message, history, ""
+
+# def load_example_user_story():
+#     with open("data/Linka#1_MusicKnowledge.md", "r") as f:
+#         return f.read()
+
+# def clustering_generator(cqs, cluster_method, n_clusters):
+#     check_api_key()
+#     if n_clusters:
+#         n_clusters = int(n_clusters)
+
+#     cqs, cq_embeddings = compute_embeddings(cqs)
+
+#     if cluster_method == "Agglomerative clustering":
+#         cq_clusters, cluster_image = agglomerative_clustering(cqs, cq_embeddings, n_clusters)
+#     else:  # cluster_method == "LLM clustering"
+#         cq_clusters, cluster_image = llm_cq_clustering(cqs, n_clusters, openai_api_key)
+
+#     return cluster_image, json.dumps(cq_clusters, indent=4)
+
+# def ontology_testing(ontology_file, ontology_desc, cqs):
+#     check_api_key()
+#     verbalisation = verbalise_ontology(ontology_file, ontology_desc, "")
+#     messages = [{
+#         "role": "system",
+#         "content": "Please (1) provide a description of the ontology uploaded to provide basic information and "
+#                    "additional context, (2) give the competency questions (CQs) that you want to test with."
+#     }, {
+#         "role": "user",
+#         "content": verbalisation + "\n" + f"Given the above ontology, please label each competency question: {cqs} to "
+#                                           f"determine whether it is addressed properly or not. Format your response in"
+#                                           f" ['yes': 'CQ1', 'no': 'CQ2', ...]."
+#     }]
+#     bot_message = chat_completion(openai_api_key, messages)
+#     return bot_message