update openai version, fix details in chitchat

src/deploy/inference_dialog_server.py

@@ -265,7 +265,7 @@ def __init__(self):
         logging.info('==>chitchat vectorizer loaded!')
         self.retrieve_query_mode = "similar"
         logging.info("Server ready")
-    def load_bert_model(self, load_mode='finetuned_bert'):
+    def load_bert_model(self, load_mode='unfinetuned_bert'):
         if load_mode=='unfinetuned_bert':
             word_embedding_model = models.Transformer('bert-base-uncased', max_seq_length=256)
             pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension())

src/gpt/gpt_interface.py

@@ -26,16 +26,16 @@ def setup_openai(fname, mode='azure'):
 
 @T.retry(stop=T.stop_after_attempt(5), wait=T.wait_fixed(60), after=lambda s: logging.error(repr(s)))
 def query_openai(prompt, mode='azure', model='gpt-35-turbo', **kwargs):
+    # 240127: update openai version
     if mode == 'openai':
-        response = openai.ChatCompletion.create(
-            model=model,
-            messages=[{'role': 'user', 'content': prompt}],
-            **kwargs
-        )
+        response = openai.chat.completions.create(model=model,
+                                             messages=[{'role': 'user', 'content': prompt}],
+                                             **kwargs
+                                             )
     else:
-        response = openai.ChatCompletion.create(
+        response = openai.chat.completions.create(
             deployment_id=model,
             messages=[{'role': 'user', 'content': prompt}],
             **kwargs,
         )
-    return response['choices'][0]['message']['content']
+    return response.choices[0].message.content

src/models/chitchat_classification.py

@@ -85,7 +85,7 @@ def sampledata_combine(data1, data2, data3, test_data3, train_count_data1=1000,
 def calculate_centroid(data, embed_method):
     if embed_method == "original":
         embeddings = np.array([bert_embed(bert_trans_model, tokenizer,text, 
-        ) for text in tqdm(data, desc="Processing with original BERT")])
+        ).cpu() for text in tqdm(data, desc="Processing with original BERT")])
     elif embed_method == "st_untrained":
         print('Using pretrained model!!!')
         embeddings = np.array([sentence_transformer_embed(unpretrained_model, text).cpu() for text in tqdm(data, desc="Processing with unpretrained sentencetransformer BERT")])
@@ -101,13 +101,14 @@ def predict_by_similarity(user_query_vector, centroids, labels):
     similarities = [cosine_similarity(user_query_vector, centroid.reshape(1, -1)) for centroid in centroids]
     return labels[np.argmax(similarities)]
 
-def plot_tsne_distribution_modified(lib_name, train_data, test_data, vectorizer, labels, c2_accuracy):
+def plot_tsne_distribution_modified(lib_name, train_data, test_data, model, labels, c2_accuracy,embed_method):
     import matplotlib.pyplot as plt
     from sklearn.manifold import TSNE
 
     # Combine train and test data for t-SNE
     combined_data = pd.concat([train_data['Question'], test_data['Question']], ignore_index=True)
-    tfidf_matrix_combined = vectorizer.transform(combined_data)
+    #tfidf_matrix_combined = vectorizer.transform(combined_data)
+    tfidf_matrix_combined = sentence_transformer_embed(model, combined_data).cpu()
 
     tsne = TSNE(n_components=2, random_state=40, init='random')
     reduced_data_combined = tsne.fit_transform(tfidf_matrix_combined)  # Fit and transform combined data
@@ -132,7 +133,7 @@ def plot_tsne_distribution_modified(lib_name, train_data, test_data, vectorizer,
     formatted_accuracy = "{:.2f}".format(c2_accuracy)
     plt.title(f't-SNE visualization of train data with test accuracy for api/non-api {formatted_accuracy}%')
     plt.legend()
-    plt.savefig(f'./plot/{lib_name}/chitchat_train_tsne_modified.png')
+    plt.savefig(f'./plot/{lib_name}/chitchat_train_tsne_modified_{embed_method}.pdf')
     plt.clf()  # Clear the current figure
 
     # Create a figure for test data
@@ -146,7 +147,7 @@ def plot_tsne_distribution_modified(lib_name, train_data, test_data, vectorizer,
 
     plt.title('t-SNE visualization of test data')
     plt.legend()
-    plt.savefig(f'./plot/{lib_name}/chitchat_test_tsne_modified.png')
+    plt.savefig(f'./plot/{lib_name}/chitchat_test_tsne_modified_{embed_method}.pdf')
 
 def main():
     process_topicalchat()
@@ -192,7 +193,17 @@ def calculate_accuracy(test_data, centroids, labels):
         correct_predictions = 0
         for index, row in test_data.iterrows():
             #user_query_vector = vectorizer.transform([row['Question']])
-            user_query_vector = bert_embed(bert_trans_model, tokenizer,row['Question'],device=device).reshape(1, -1)
+            if args.embed_method == "original":
+                print('using original')
+                user_query_vector = np.array([bert_embed(bert_trans_model, tokenizer,[row['Question']], )])
+            elif args.embed_method == "st_untrained":
+                print('Using pretrained model!!!')
+                user_query_vector = np.array([sentence_transformer_embed(unpretrained_model, [row['Question']]).cpu()])
+            elif args.embed_method == "st_trained":
+                print('using finetuned model')
+                user_query_vector = np.array([sentence_transformer_embed(pretrained_model, [row['Question']]).cpu()])
+
+            user_query_vector = user_query_vector.flatten().reshape(1,-1)
             predicted_label = predict_by_similarity(user_query_vector, centroids, labels)
             actual_label = row['Source']
             if predicted_label == actual_label:
@@ -205,7 +216,16 @@ def calculate_accuracy(test_data, centroids, labels):
     correct_predictions = 0
     for index, row in test_data.iterrows():
         #user_query_vector = vectorizer.transform([row['Question']])
-        user_query_vector = bert_embed(bert_trans_model, tokenizer,row['Question'],device=device).reshape(1, -1)
+        #user_query_vector = bert_embed(bert_trans_model, tokenizer,row['Question'],device=device).reshape(1, -1)
+        if args.embed_method == "original":
+            user_query_vector = np.array(bert_embed(bert_trans_model, tokenizer,[row['Question']], ))
+        elif args.embed_method == "st_untrained":
+            print('Using pretrained model!!!')
+            user_query_vector = np.array(sentence_transformer_embed(unpretrained_model, [row['Question']]).cpu())
+        elif args.embed_method == "st_trained":
+            user_query_vector = np.array(sentence_transformer_embed(pretrained_model, [row['Question']]).cpu())
+        user_query_vector = user_query_vector.flatten().reshape(1,-1)
+
         predicted_label = predict_by_similarity(user_query_vector, centroids, labels)
         actual_label = row['Source']
         if (actual_label=='api-query' and predicted_label=='api-query') or (actual_label!='api-query' and predicted_label!='api-query'):
@@ -223,6 +243,12 @@ def calculate_accuracy(test_data, centroids, labels):
     os.makedirs(f"./plot/{args.LIB}", exist_ok=True)
     print(f"Centroids saved. Time taken: {time.time() - start_time:.2f} seconds")
     start_time = time.time()
+    if args.embed_method == "original":
+        plot_tsne_distribution_modified(args.LIB, train_data, test_data, bert_trans_model, labels, c2_accuracy,args.embed_method)
+    elif args.embed_method == "st_untrained":
+        plot_tsne_distribution_modified(args.LIB, train_data, test_data, unpretrained_model, labels, c2_accuracy,args.embed_method)
+    elif args.embed_method == "st_trained":
+        plot_tsne_distribution_modified(args.LIB, train_data, test_data, pretrained_model, labels, c2_accuracy,args.embed_method)
 
 if __name__=='__main__':
     main()

src/models/model.py

@@ -21,11 +21,6 @@ def create_peft_config(model):
     return model, peft_config
 
 def LLM_model(local=True):
-    """
-    https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai
-    https://python.langchain.com/docs/modules/model_io/models/llms/integrations/huggingface_hub
-    https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads
-    """
     if llm_model_dict[LLM_MODEL]['platform']=='OPENAI':
         from gpt import gpt_interface
         gpt_interface.setup_openai('', mode='openai')
@@ -121,8 +116,8 @@ def embedding_model():
     return embeddings
 
 if __name__=='__main__':
-   llm, tokenizer =LLM_model()
-   prompt = "hello"
-   response, history = LLM_response(llm,tokenizer,prompt)
-   print(f'User: {prompt}')
-   print(f'LLM: {response}')
+    llm, tokenizer =LLM_model()
+    prompt = "hello"
+    response, history = LLM_response(llm,tokenizer,prompt)
+    print(f'User: {prompt}')
+    print(f'LLM: {response}')

src/requirements.txt

@@ -24,7 +24,7 @@ mmtf_python==1.1.3
 nbformat==5.9.2
 networkx==3.2.1
 numpy==1.26.1
-openai==0.28.1
+openai==1.10.0
 pandas
 peft==0.6.0
 protobuf==3.20.0