openai4

analyzer.py

@@ -15,7 +15,7 @@ def analyze_code(code: str) -> str:
     Returns the analysis as a string.
     """
     from openai import OpenAI
-    client = OpenAI(api_key=os.getenv("modal_api"))
+    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. "
@@ -27,7 +27,7 @@ def analyze_code(code: str) -> str:
         "{\n  'strength': '...', \n  'weaknesses': '...', \n  'speciality': '...', \n  'relevance rating': 'high'\n}"
     )
     response = client.chat.completions.create(
-        model="Orion-zhen/Qwen2.5-Coder-7B-Instruct-AWQ",  # Updated model
+        model="openai/gpt-4.1-nano",  # Updated to GPT-4.1 Nano model
         messages=[
             {"role": "system", "content": system_prompt},
             {"role": "user", "content": code}
@@ -235,7 +235,7 @@ def analyze_code_chunk(code: str, user_requirements: str = "") -> str:
     Analyzes a code chunk and returns a JSON summary for that chunk.
     """
     from openai import OpenAI
-    client = OpenAI(api_key=os.getenv("modal_api"))
+    client = OpenAI(api_key=os.getenv("OpenAI_API"))
     client.base_url = os.getenv("base_url")
     
     # Build the user requirements section
@@ -256,12 +256,11 @@ def analyze_code_chunk(code: str, user_requirements: str = "") -> str:
     )
     
     response = client.chat.completions.create(
-        model="Orion-zhen/Qwen2.5-Coder-7B-Instruct-AWQ",
+        model="openai/gpt-4.1-nano",
         messages=[
             {"role": "system", "content": chunk_prompt},
             {"role": "user", "content": code}
         ],
-
         temperature=0.4
     )
     return response.choices[0].message.content
@@ -271,7 +270,7 @@ def aggregate_chunk_analyses(chunk_jsons: list, user_requirements: str = "") ->
     Aggregates a list of chunk JSONs into a single JSON summary using the LLM.
     """
     from openai import OpenAI
-    client = OpenAI(api_key=os.getenv("modal_api"))
+    client = OpenAI(api_key=os.getenv("OpenAI_API"))
     client.base_url = os.getenv("base_url")
     
     # Build the user requirements section
@@ -292,7 +291,7 @@ def aggregate_chunk_analyses(chunk_jsons: list, user_requirements: str = "") ->
     )
     user_content = "Here are the chunk analyses:\n" + "\n".join(chunk_jsons)
     response = client.chat.completions.create(
-        model="Orion-zhen/Qwen2.5-Coder-7B-Instruct-AWQ",
+        model="openai/gpt-4.1-nano",
         messages=[
             {"role": "system", "content": aggregation_prompt},
             {"role": "user", "content": user_content}
@@ -329,7 +328,7 @@ def analyze_repo_chunk_for_context(chunk: str, repo_id: str) -> str:
     """
     try:
         from openai import OpenAI
-        client = OpenAI(api_key=os.getenv("modal_api"))
+        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.
@@ -349,7 +348,7 @@ Repository chunk:
 Provide a clear, conversational summary in 2-3 paragraphs:"""
         
         response = client.chat.completions.create(
-            model="Orion-zhen/Qwen2.5-Coder-7B-Instruct-AWQ",
+            model="openai/gpt-4.1-nano",
             messages=[
                 {"role": "system", "content": "You are an expert code analyst creating conversational summaries for a repository assistant chatbot."},
                 {"role": "user", "content": context_prompt}
@@ -385,7 +384,7 @@ def create_repo_context_summary(repo_content: str, repo_id: str) -> str:
         # Create final comprehensive summary
         try:
             from openai import OpenAI
-            client = OpenAI(api_key=os.getenv("modal_api"))
+            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.
@@ -403,7 +402,7 @@ Create a well-structured overview covering:
 Make this comprehensive but conversational - it will be used by a chatbot to answer user questions about the repository."""
             
             response = client.chat.completions.create(
-                model="Orion-zhen/Qwen2.5-Coder-7B-Instruct-AWQ",
+                model="openai/gpt-4.1-nano",
                 messages=[
                     {"role": "system", "content": "You are creating a comprehensive repository summary for a chatbot assistant."},
                     {"role": "user", "content": final_prompt}

app.py

@@ -124,11 +124,11 @@ Selected repositories:"""
 
         try:
             from openai import OpenAI
-            client = OpenAI(api_key=os.getenv("modal_api"))
+            client = OpenAI(api_key=os.getenv("OpenAI_API"))
             client.base_url = os.getenv("base_url")
             
             response = client.chat.completions.create(
-                model="Orion-zhen/Qwen2.5-Coder-7B-Instruct-AWQ",
+                model="openai/gpt-4.1-nano",
                 messages=[
                     {"role": "system", "content": "You are an expert at analyzing and ranking repositories based on user requirements. Always return valid JSON."},
                     {"role": "user", "content": prompt}

chatbot_page.py

@@ -17,7 +17,7 @@ conversation_history = []
 # Function to handle chat
 def chat_with_user(user_message, history):
     from openai import OpenAI
-    client = OpenAI(api_key=os.getenv("modal_api"))
+    client = OpenAI(api_key=os.getenv("OpenAI_API"))
     client.base_url = os.getenv("base_url")
     # Build the message list for the LLM
     messages = [
@@ -29,7 +29,7 @@ def chat_with_user(user_message, history):
             messages.append({"role": "assistant", "content": msg[1]})
     messages.append({"role": "user", "content": user_message})
     response = client.chat.completions.create(
-        model="Orion-zhen/Qwen2.5-Coder-7B-Instruct-AWQ",
+        model="openai/gpt-4.1-nano",
         messages=messages,
         max_tokens=256,
         temperature=0.7
@@ -40,7 +40,7 @@ def chat_with_user(user_message, history):
 # Function to end chat and extract keywords
 def extract_keywords_from_conversation(history):
     from openai import OpenAI
-    client = OpenAI(api_key=os.getenv("modal_api"))
+    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]])
@@ -56,7 +56,7 @@ def extract_keywords_from_conversation(history):
         "Conversation:\n" + conversation + "\n\nExtract about 5 keywords for Hugging Face repo search."
     )
     response = client.chat.completions.create(
-        model="Orion-zhen/Qwen2.5-Coder-7B-Instruct-AWQ",
+        model="openai/gpt-4.1-nano",
         messages=[
             {"role": "system", "content": system_prompt},
             {"role": "user", "content": user_prompt}

repo_explorer.py

@@ -275,11 +275,11 @@ Answer the user's question based on your comprehensive knowledge of this reposit
     
     try:
         from openai import OpenAI
-        client = OpenAI(api_key=os.getenv("modal_api"))
+        client = OpenAI(api_key=os.getenv("OpenAI_API"))
         client.base_url = os.getenv("base_url")
         
         response = client.chat.completions.create(
-            model="Orion-zhen/Qwen2.5-Coder-7B-Instruct-AWQ",
+            model="openai/gpt-4.1-nano",
             messages=[
                 {"role": "system", "content": repo_system_prompt},
                 {"role": "user", "content": user_message}