Update app.py

app.py

@@ -17,8 +17,8 @@ api_key = os.getenv("OPENAI_API_KEY")
 if not api_key:
     raise ValueError("OPENAI_API_KEY environment variable is not set")
 
-# Initialize OpenAI client with minimal configuration
-client = openai.OpenAI(api_key=api_key)
+# Initialize OpenAI client with older API syntax
+openai.api_key = api_key
 
 # Custom audio recorder component
 def create_audio_recorder():
@@ -136,7 +136,7 @@ class AdvancedRAG:
         self.assistant_id: Optional[str] = os.getenv("ASSISTANT_ID")
 
     def create_thread(self) -> str:
-        thread = client.beta.threads.create(
+        thread = openai.Thread.create(
             tool_resources={
                 "file_search": {
                     "vector_store_ids": [self.vector_store_id]
@@ -148,7 +148,7 @@ class AdvancedRAG:
 
     def create_vector_store(self, name: str = "My Vector Store") -> str:
         try:
-            vector_store = client.vector_stores.create(name=name)
+            vector_store = openai.VectorStore.create(name=name)
             self.vector_store_id = vector_store.id
             return self.vector_store_id
         except Exception as e:
@@ -157,11 +157,11 @@ class AdvancedRAG:
     def upload_document(self, file) -> str:
         if self.vector_store_id:
             try:
-                client.vector_stores.delete(self.vector_store_id)
+                openai.VectorStore.delete(self.vector_store_id)
             except Exception as e:
                 print(f"Could not delete previous vector store {self.vector_store_id}: {e}")
         
-        vector_store = client.vector_stores.create(name="knowledge_base")
+        vector_store = openai.VectorStore.create(name="knowledge_base")
         self.vector_store_id = vector_store.id
         self.file_ids = []
 
@@ -169,17 +169,17 @@ class AdvancedRAG:
             tmp.write(file.read())
             tmp.flush()
             with open(tmp.name, "rb") as file_obj:
-                file_obj = client.files.create(
+                file_obj = openai.File.create(
                     file=file_obj,
                     purpose="assistants"
                 )
                 self.file_ids.append(file_obj.id)
-                client.vector_stores.files.create(
+                openai.VectorStore.files.create(
                     vector_store_id=self.vector_store_id,
                     file_id=file_obj.id
                 )
 
-        thread = client.beta.threads.create(
+        thread = openai.Thread.create(
             tool_resources={
                 "file_search": {
                     "vector_store_ids": [self.vector_store_id]
@@ -191,20 +191,20 @@ class AdvancedRAG:
 
     def ask_question(self, question: str) -> str:
         try:
-            client.beta.threads.messages.create(
+            openai.Thread.messages.create(
                 thread_id=self.thread_id,
                 role="user",
                 content=question
             )
             
-            run = client.beta.threads.runs.create(
+            run = openai.Thread.runs.create(
                 thread_id=self.thread_id,
                 assistant_id=self.assistant_id
             )
             
             waited = 0
             while True:
-                run_status = client.beta.threads.runs.retrieve(
+                run_status = openai.Thread.runs.retrieve(
                     thread_id=self.thread_id,
                     run_id=run.id
                 )
@@ -217,7 +217,7 @@ class AdvancedRAG:
                 if waited > 60:
                     raise Exception("Run timed out after 60 seconds.")
 
-            messages = client.beta.threads.messages.list(
+            messages = openai.Thread.messages.list(
                 thread_id=self.thread_id,
                 order='desc',
                 limit=1
@@ -235,7 +235,7 @@ class AdvancedRAG:
                 tmp.flush()
                 tmp_path = tmp.name
             with open(tmp_path, "rb") as audio:
-                transcript = client.audio.transcriptions.create(
+                transcript = openai.Audio.transcriptions.create(
                     model="whisper-1",
                     file=audio,
                     language="en"
@@ -293,7 +293,7 @@ def process_audio_base64(audio_base64, history):
 
         # Transcribe audio
         with open(tmp_path, "rb") as audio_file:
-            transcript = client.audio.transcriptions.create(
+            transcript = openai.Audio.transcriptions.create(
                 model="whisper-1",
                 file=audio_file,
                 language="en"