Upload app.py

app.py

@@ -9,7 +9,7 @@ from openai import OpenAI
 from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings
 from langchain_community.vectorstores import Chroma
 
-from huggingface_hub import CommitScheduler
+# from huggingface_hub import CommitScheduler
 from pathlib import Path
 
 
@@ -35,16 +35,16 @@ retriever = vectorstore_persisted.as_retriever(
 
 # Prepare the logging functionality
 
-log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
-log_folder = log_file.parent
+# log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
+# log_folder = log_file.parent
 
-scheduler = CommitScheduler(
-    repo_id="document-qna-chroma-anyscale-logs",
-    repo_type="dataset",
-    folder_path=log_folder,
-    path_in_repo="data",
-    every=2
-)
+# scheduler = CommitScheduler(
+#     repo_id="document-qna-chroma-anyscale-logs",
+#     repo_type="dataset",
+#     folder_path=log_folder,
+#     path_in_repo="data",
+#     every=2
+# )
 
 qna_system_message = """
 You are an assistant to a coder. Your task is to provide relevant information about the Python package Streamlit.
@@ -102,7 +102,8 @@ def predict(user_input):
 
     try:
         response = client.chat.completions.create(
-            model='mistralai/Mixtral-8x7B-Instruct-v0.1',
+            # model='mistralai/Mixtral-8x7B-Instruct-v0.1',
+            model='gpt-35-turbo'
             messages=prompt,
             temperature=0
         )
@@ -116,16 +117,16 @@ def predict(user_input):
     # While writing to the log file, ensure that the commit scheduler is locked to avoid parallel
     # access
     
-    with scheduler.lock:
-        with log_file.open("a") as f:
-            f.write(json.dumps(
-                {
-                    'user_input': user_input,
-                    'retrieved_context': context_for_query,
-                    'model_response': prediction
-                }
-            ))
-            f.write("\n")
+    # with scheduler.lock:
+    #     with log_file.open("a") as f:
+    #         f.write(json.dumps(
+    #             {
+    #                 'user_input': user_input,
+    #                 'retrieved_context': context_for_query,
+    #                 'model_response': prediction
+    #             }
+    #         ))
+    #         f.write("\n")
     
     return prediction