Update app/main.py

app/main.py

@@ -261,6 +261,36 @@ def init_vertex_ai():
                 print(f"ERROR: Failed to initialize client with credentials from Credential Manager file ({credential_manager.credentials_dir}): {e}")
         else:
              print(f"INFO: No credentials loaded via Credential Manager.")
+
+        # Priority 3: Fall back to GOOGLE_APPLICATION_CREDENTIALS environment variable (file path)
+        file_path = os.environ.get("GOOGLE_APPLICATION_CREDENTIALS")
+        if file_path:
+            print(f"INFO: Checking GOOGLE_APPLICATION_CREDENTIALS file path: {file_path}")
+            if os.path.exists(file_path):
+                try:
+                    print(f"INFO: File exists, attempting to load credentials")
+                    credentials = service_account.Credentials.from_service_account_file(
+                        file_path,
+                        scopes=['https://www.googleapis.com/auth/cloud-platform']
+                    )
+                    project_id = credentials.project_id
+                    print(f"Successfully loaded credentials from file for project: {project_id}")
+                    
+                    try:
+                        # Initialize the global client ONLY if it hasn't been set yet
+                        if client is None:
+                            client = genai.Client(vertexai=True, credentials=credentials, project=project_id, location="us-central1")
+                            print(f"INFO: Initialized fallback Vertex AI client using GOOGLE_APPLICATION_CREDENTIALS file path for project: {project_id}")
+                            return True # Successfully initialized global client
+                        else:
+                            print(f"INFO: Fallback client already initialized. GOOGLE_APPLICATION_CREDENTIALS validated for project: {project_id}")
+                            # If client was already set, we don't need to return True, just let it finish
+                    except Exception as client_err:
+                        print(f"ERROR: Failed to initialize client with credentials from GOOGLE_APPLICATION_CREDENTIALS file ({file_path}): {client_err}")
+                except Exception as e:
+                    print(f"ERROR: Failed to load credentials from GOOGLE_APPLICATION_CREDENTIALS path ({file_path}): {e}") # Added context
+            else:
+                print(f"ERROR: GOOGLE_APPLICATION_CREDENTIALS file does not exist at path: {file_path}")
         
         # If none of the methods worked, this error is still useful
         # If we reach here, either no method worked, or a prior method already initialized the client
@@ -416,6 +446,11 @@ def create_gemini_prompt(messages: List[OpenAIMessage]) -> Union[types.Content,
     
     # Process all messages in their original order
     for idx, message in enumerate(messages):
+        # Skip messages with empty content
+        if not message.content:
+            print(f"Skipping message {idx} due to empty content (Role: {message.role})")
+            continue
+
         # Map OpenAI roles to Gemini roles
         role = message.role
         
@@ -449,7 +484,8 @@ def create_gemini_prompt(messages: List[OpenAIMessage]) -> Union[types.Content,
             for part in message.content:
                 if isinstance(part, dict):
                     if part.get('type') == 'text':
-                        parts.append(types.Part(text=part.get('text', '')))
+                        print("Empty message detected. Auto fill in.")
+                        parts.append(types.Part(text=part.get('text', '\n')))
                     elif part.get('type') == 'image_url':
                         image_url = part.get('image_url', {}).get('url', '')
                         if image_url.startswith('data:'):
@@ -1083,6 +1119,7 @@ async def chat_completions(request: OpenAIRequest, api_key: str = Depends(get_ap
                     try:
                         for candidate_index in range(candidate_count):
                             print(f"Sending streaming request to Gemini API (Model: {model_name}, Prompt Format: {prompt_func.__name__})")
+                            # print(prompt)
                             responses = await client_instance.aio.models.generate_content_stream( # Use client_instance
                                 model=model_name,
                                 contents=prompt,