Fix: Expand API retry logic to cover response processing.
The retry mechanism in `api_call` now encloses both the API call itself (`client.models.generate_content`) and the subsequent processing of the response (extraction of text and image data). This addresses an issue where a 'NoneType' error could occur during response processing after a technically successful API call, and this error would not trigger a retry. The `api_call` function will now attempt the entire sequence (call + process) up to 3 times with exponential backoff (1s, 2s) if any exception occurs during this sequence.
This commit is contained in:
google-labs-jules[bot]
+106
-53
@@ -321,34 +321,76 @@ class SSL_GeminiTextPrompt:
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except Exception as seed_error:
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print(f"[WARNING] Failed to set seed for API request: {str(seed_error)}")
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text_output = ""
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image_tensor = None
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# text_output = "" # Will be initialized in api_call and retrieved from queue
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# image_tensor = None # Will be initialized in api_call and retrieved from queue
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try:
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print(f"[INFO] Sending API request to Gemini")
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start_time = time.time()
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timeout = 30
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timeout = 30 # Timeout for queue.get()
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def api_call():
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last_exception = None
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max_retries = 3
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for attempt in range(max_retries):
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current_text_output = ""
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current_image_tensor = None # Initialized for each attempt
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try:
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print(f"[INFO] API call attempt {attempt + 1}/{max_retries}")
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api_response = client.models.generate_content(
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model=model,
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contents=contents,
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config=generate_content_config,
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)
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result_queue.put(("success", api_response))
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print(f"[INFO] Received API response for attempt {attempt + 1}")
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if hasattr(api_response, 'candidates') and api_response.candidates:
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for part in api_response.candidates[0].content.parts:
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if hasattr(part, 'text') and part.text is not None:
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current_text_output += part.text
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elif hasattr(part, 'inline_data') and part.inline_data is not None:
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try:
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inline_data = part.inline_data
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mime_type = inline_data.mime_type
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data = inline_data.data
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image_path = self.save_binary_file(data, mime_type)
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img = Image.open(image_path)
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if img.mode != 'RGB':
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img = img.convert('RGB')
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img_array = np.array(img).astype(np.float32) / 255.0
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current_image_tensor = torch.from_numpy(img_array).unsqueeze(0)
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except Exception as img_e:
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print(f"[ERROR] Image processing error during attempt {attempt + 1}: {str(img_e)}")
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current_text_output += f"\nImage processing error: {str(img_e)}"
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if current_image_tensor is None:
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current_image_tensor = self.generate_empty_image()
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if current_image_tensor is None: # Ensure image_tensor is always set
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current_image_tensor = self.generate_empty_image()
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result_queue.put(("success", (current_text_output, current_image_tensor)))
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print(f"[INFO] Attempt {attempt + 1}/{max_retries} successful.")
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return # Success, exit the function
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except Exception as e:
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print(f"[ERROR] API call attempt {attempt + 1}/{max_retries} failed: {str(e)}")
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last_exception = e
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print(f"[ERROR] Attempt {attempt + 1}/{max_retries} failed: {str(e)}")
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if attempt < max_retries - 1:
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time.sleep(1) # Wait 1 second before retrying
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else:
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print(f"[ERROR] All {max_retries} API call attempts failed.")
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result_queue.put(("error", e))
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return # All retries failed
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wait_time = 2**attempt # Exponential backoff: 1s, 2s
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print(f"[INFO] Waiting {wait_time}s before next attempt...")
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time.sleep(wait_time)
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# On the last attempt, the loop will end, and we'll put the error in the queue outside the loop.
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# If all attempts failed
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print(f"[ERROR] All {max_retries} API call and processing attempts failed. Last error: {str(last_exception)}")
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result_queue.put(("error", last_exception))
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result_queue = queue.Queue()
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@@ -356,66 +398,77 @@ class SSL_GeminiTextPrompt:
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api_thread.daemon = True
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api_thread.start()
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text_output = ""
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image_tensor = self.generate_empty_image() # Default to empty image
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try:
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status, result = result_queue.get(timeout=timeout)
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elapsed_time = time.time() - start_time
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if status == "success":
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response = result
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print(f"[INFO] API request successfully completed in {elapsed_time:.2f} seconds")
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else:
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print(f"[ERROR] Error in API request thread, time taken: {elapsed_time:.2f} seconds, error: {str(result)}")
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error_str = str(result).lower()
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text_output, image_tensor = result # result is now a tuple (text_output, image_tensor)
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print(f"[INFO] API request and processing successfully completed in {elapsed_time:.2f} seconds")
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else: # status == "error"
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error_exception = result
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print(f"[ERROR] Error in API request/processing thread after {elapsed_time:.2f} seconds: {str(error_exception)}")
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text_output = f"API call/processing error: {str(error_exception)}"
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# image_tensor is already self.generate_empty_image()
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error_str = str(error_exception).lower()
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if any(term in error_str for term in ["timeout", "connection", "network", "socket", "连接", "网络"]):
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if not network_ok and use_proxy != "True":
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return (f"API request failed: {str(result)}. Network connection test failed, consider enabling proxy.", self.generate_empty_image(), actual_seed if actual_seed is not None else 0)
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raise result
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text_output += " Network connection test failed, consider enabling proxy."
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# return (f"API request failed: {str(error_exception)}. Network connection test failed, consider enabling proxy.", self.generate_empty_image(), actual_seed if actual_seed is not None else 0)
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# raise result # No need to raise here, already handled by setting text_output
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except queue.Empty:
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elapsed_time = time.time() - start_time
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print(f"[ERROR] API request timed out, waited: {elapsed_time:.2f} seconds")
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print(f"[ERROR] API request/processing timed out in main thread, waited: {elapsed_time:.2f} seconds")
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timeout_msg = f"Gemini API request timed out, waited {timeout} seconds."
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timeout_msg = f"Gemini API request/processing timed out, waited {timeout} seconds."
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if not network_ok:
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if use_proxy == "True":
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timeout_msg += f"Network connection test failed, the current proxy ({proxy_host}:{proxy_port}) may be invalid, please check proxy settings."
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timeout_msg += f" Network connection test failed, the current proxy ({proxy_host}:{proxy_port}) may be invalid, please check proxy settings."
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else:
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timeout_msg += "Network connection test failed, consider enabling proxy."
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timeout_msg += " Network connection test failed, consider enabling proxy."
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else:
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timeout_msg += "Network connection test successful, but API request still timed out. This could be due to a busy server or a large request."
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return (timeout_msg, self.generate_empty_image(), actual_seed if actual_seed is not None else 0)
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timeout_msg += " Network connection test successful, but API request/processing still timed out. This could be due to a busy server or a large request."
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text_output = timeout_msg
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# image_tensor is already self.generate_empty_image()
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# return (timeout_msg, self.generate_empty_image(), actual_seed if actual_seed is not None else 0)
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print(f"[INFO] Received API response")
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# print(f"[INFO] Received API response") # This line is now part of api_call or success message
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if hasattr(response, 'candidates') and response.candidates:
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for part in response.candidates[0].content.parts:
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if hasattr(part, 'text') and part.text is not None:
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text_output += part.text
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elif hasattr(part, 'inline_data') and part.inline_data is not None:
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try:
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inline_data = part.inline_data
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mime_type = inline_data.mime_type
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data = inline_data.data
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image_path = self.save_binary_file(data, mime_type)
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img = Image.open(image_path)
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if img.mode != 'RGB':
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img = img.convert('RGB')
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img_array = np.array(img).astype(np.float32) / 255.0
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image_tensor = torch.from_numpy(img_array).unsqueeze(0)
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except Exception as e:
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print(f"[ERROR] Image processing error: {str(e)}")
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text_output += f"\nImage processing error: {str(e)}"
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except Exception as e:
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print(f"[ERROR] API call error: {str(e)}")
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text_output = f"API call error: {str(e)}"
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# The following block is now inside api_call
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# if hasattr(response, 'candidates') and response.candidates:
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# for part in response.candidates[0].content.parts:
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# if hasattr(part, 'text') and part.text is not None:
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# text_output += part.text
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#
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# elif hasattr(part, 'inline_data') and part.inline_data is not None:
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# try:
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# inline_data = part.inline_data
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# mime_type = inline_data.mime_type
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# data = inline_data.data
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#
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# image_path = self.save_binary_file(data, mime_type)
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#
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# img = Image.open(image_path)
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#
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# if img.mode != 'RGB':
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# img = img.convert('RGB')
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#
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# img_array = np.array(img).astype(np.float32) / 255.0
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# image_tensor = torch.from_numpy(img_array).unsqueeze(0)
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# except Exception as e:
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# print(f"[ERROR] Image processing error: {str(e)}")
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# text_output += f"\nImage processing error: {str(e)}"
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except Exception as e: # Catch-all for unforeseen errors in the main try block
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print(f"[ERROR] Unhandled error in generate method: {str(e)}")
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text_output = f"Unhandled error: {str(e)}"
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if image_tensor is None: # Ensure image_tensor is set
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image_tensor = self.generate_empty_image()
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if image_tensor is None:
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image_tensor = self.generate_empty_image()
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# if image_tensor is None: # This should be handled by initialization or in api_call
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# image_tensor = self.generate_empty_image()
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# if use_seed == "True" and actual_seed is not None:
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# seed_info = f"\n\n[Seed information: {actual_seed}]"
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