Fixed generate_response

Fixed the error saying Probabilities Do Not Sum

chatbotTrainer.py

@@ -225,8 +225,8 @@ class ChatbotTrainer:
 
         # Prediction Setup (Everything here will take priority)
         self.min_word = 10      # Only for generate_response
-        self.temperature = 0.8
-        self.scaling_factor = 100
+        self.temperature = 1
+        self.scaling_factor = 1
         self.logger = self.setup_logger()  # Initialize your logger here
         self.beam_width = 9
         self.top_p = 0.7
@@ -789,12 +789,14 @@ class ChatbotTrainer:
                 output_tokens, state_h, state_c = self.decoder_model.predict([target_seq, state_h, state_c])
 
                 # Scale logits immediately after getting output_tokens
-                logits = output_tokens[0, -1, :]    # * self.scaling_factor
-                # logits = logits / self.temperature
+                logits = output_tokens[0, -1, :] * self.scaling_factor
+                logits = logits / self.temperature
+                logits = np.clip(logits, -50, 50)
 
                 # Compute softmax
                 exp_logits = np.exp(logits - np.max(logits))  # Prevent overflow
                 probabilities = exp_logits / np.sum(exp_logits)
+                probabilities = exp_logits / (np.sum(exp_logits) + 1e-8)
 
                 predicted_token_index = np.random.choice(len(probabilities), p=probabilities)
                 predicted_word = self.reverse_tokenizer.get(predicted_token_index, '<oov>')

runCorpus.py

@@ -1,4 +1,4 @@
-# self.runMovieCorpus.py
+# runMovieCorpus.py
 import os
 from playsound3 import playsound
 import tensorflow
@@ -182,7 +182,7 @@ class CorpusTrainer:
 		self.failure_history.append(len(self.troubleList))
 		if percent_running is None:
 			percent_running = 0.0
-		self.chatbot_trainer.logger.info(f"self.Running Percentage Failure: {percent_running}%")
+		self.chatbot_trainer.logger.info(f"Running Percentage Failure: {percent_running}%")
 
 		# We check for speaker vs top num achieved successfully in self.speakerList
 		# topConvo is a larger buffer for models that may take longer to learn but for dynamic learning. self.top_num is for running consecutive and is default in that situation
@@ -241,15 +241,15 @@ class CorpusTrainer:
 		# Debug Lines; But pretty useful to see how it works
 		print(f"Trouble List: {len(self.troubleList)}")
 		print(f"Bad Count: {self.bad_count}")
-		print(f"Number of Conversations(This self.Run): {self.counter}")
+		print(f"Number of Conversations(This Run): {self.counter}")
 		print(f"Number of Conversations Combined: {topConvo}")
-		print(f"self.Running Trouble: {len(self.runningTrouble)}")
+		print(f"Running Trouble: {len(self.runningTrouble)}")
 
 		percent_running = self.runningPercent(len(self.troubleList), topConvo)
 		self.failure_history.append(len(self.troubleList))
 		if percent_running is None:
 			percent_running = 0.0
-		self.chatbot_trainer.logger.info(f"self.Running Percentage Failure: {percent_running}%")
+		self.chatbot_trainer.logger.info(f"Running Percentage Failure: {percent_running}%")
 
 		print(f"Now is the time to quit if need be...  ")
 		if play_notification == 1:
@@ -259,14 +259,14 @@ class CorpusTrainer:
 			print(f"Next convo in:{self.time_sleep-x}")
 
 		if percent_running is not None:
-			# Note: The self.run adds to the trained speaker list which is successful trainings. If it's not in that list, when it comes up again it will be trained another time.
+			# Note: The run adds to the trained speaker list which is successful trainings. If it's not in that list, when it comes up again it will be trained another time.
 			if percent_running > self.percent_reset:
 				self.log_failures(len(self.troubleList), self.log_file)
 				print("Plotting Failures... See failures_plot.png for more information...  ")
 				self.plot_failures(self.log_file)
 				if play_notification == 1:
 					playsound(notification_sound)
-				print(f"Resetting... Failure Rate is Greater than {self.percent_reset}%...  For this self.run.")
+				print(f"Resetting... Failure Rate is Greater than {self.percent_reset}%...  For this run.")
 				# time.sleep(self.time_sleep * 3)
 				return self.main(self.chatbot_trainer, user_choice, dialog_data, topConvo, self.top_num)
 
@@ -340,8 +340,8 @@ class CorpusTrainer:
 
 		# Plot actual failure values
 		plt.figure(figsize=(10, 6))
-		plt.plot(self.failure_history, marker='o', linestyle='-', color='red', label='Failures Per self.Run')
-		plt.xlabel("self.Run Iteration")
+		plt.plot(self.failure_history, marker='o', linestyle='-', color='red', label='Failures Per Run')
+		plt.xlabel("Run Iteration")
 		plt.ylabel("Number of Failures")
 		plt.title("Failures Before Restart Over Time")
 		plt.legend()