base_url_gone

analyzer.py

@@ -16,7 +16,6 @@ def analyze_code(code: str) -> str:
     """
     from openai import OpenAI
     client = OpenAI(api_key=os.getenv("OpenAI_API"))
-    client.base_url = os.getenv("base_url")
     system_prompt = (
         "You are a highly precise and strict JSON generator. Analyze the code given to you. "
         "Your ONLY output must be a valid JSON object with the following keys: 'strength', 'weaknesses', 'speciality', 'relevance rating'. "
@@ -236,7 +235,6 @@ def analyze_code_chunk(code: str, user_requirements: str = "") -> str:
     """
     from openai import OpenAI
     client = OpenAI(api_key=os.getenv("OpenAI_API"))
-    client.base_url = os.getenv("base_url")
     
     # Build the user requirements section
     requirements_section = ""
@@ -271,7 +269,6 @@ def aggregate_chunk_analyses(chunk_jsons: list, user_requirements: str = "") ->
     """
     from openai import OpenAI
     client = OpenAI(api_key=os.getenv("OpenAI_API"))
-    client.base_url = os.getenv("base_url")
     
     # Build the user requirements section
     requirements_section = ""
@@ -329,7 +326,6 @@ def analyze_repo_chunk_for_context(chunk: str, repo_id: str) -> str:
     try:
         from openai import OpenAI
         client = OpenAI(api_key=os.getenv("OpenAI_API"))
-        client.base_url = os.getenv("base_url")
         
         context_prompt = f"""You are analyzing a chunk of code from the repository '{repo_id}' to create a conversational summary for a chatbot assistant.
 
@@ -385,7 +381,6 @@ def create_repo_context_summary(repo_content: str, repo_id: str) -> str:
         try:
             from openai import OpenAI
             client = OpenAI(api_key=os.getenv("OpenAI_API"))
-            client.base_url = os.getenv("base_url")
             
             final_prompt = f"""Based on the following section summaries of repository '{repo_id}', create a comprehensive overview that a chatbot can use to answer user questions.
 

app.py

@@ -125,7 +125,7 @@ Selected repositories:"""
         try:
             from openai import OpenAI
             client = OpenAI(api_key=os.getenv("OpenAI_API"))
-            client.base_url = os.getenv("base_url")
+            # client.base_url = os.getenv("base_url")
             
             response = client.chat.completions.create(
                 model="openai/gpt-4.1-nano",

chatbot_page.py

@@ -18,7 +18,6 @@ conversation_history = []
 def chat_with_user(user_message, history):
     from openai import OpenAI
     client = OpenAI(api_key=os.getenv("OpenAI_API"))
-    client.base_url = os.getenv("base_url")
     # Build the message list for the LLM
     messages = [
         {"role": "system", "content": CHATBOT_SYSTEM_PROMPT}
@@ -41,7 +40,6 @@ def chat_with_user(user_message, history):
 def extract_keywords_from_conversation(history):
     from openai import OpenAI
     client = OpenAI(api_key=os.getenv("OpenAI_API"))
-    client.base_url = os.getenv("base_url")
     # Combine all user and assistant messages into a single string
     conversation = "\n".join([f"User: {msg[0]}\nAssistant: {msg[1]}" for msg in history if msg[1]])
     system_prompt = (

repo_explorer.py

@@ -276,7 +276,6 @@ Answer the user's question based on your comprehensive knowledge of this reposit
     try:
         from openai import OpenAI
         client = OpenAI(api_key=os.getenv("OpenAI_API"))
-        client.base_url = os.getenv("base_url")
         
         response = client.chat.completions.create(
             model="openai/gpt-4.1-nano",