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@ -464,6 +464,7 @@ class AIPersonality:
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"""
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return self.multichoice_question(question, ["no","yes"], context, max_answer_length, conditionning=conditionning)>0
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def multichoice_question(self, question: str, possible_answers:list, context:str = "", max_answer_length: int = 50, conditionning="", return_explanation=False) -> int:
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"""
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Interprets a multi-choice question from a users response. This function expects only one choice as true. All other choices are considered false. If none are correct, returns -1.
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@ -477,43 +478,58 @@ class AIPersonality:
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Returns:
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int: Index of the selected option within the possible_ansers list. Or -1 if there was not match found among any of them.
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"""
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start_header_id_template = self.config.start_header_id_template
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end_header_id_template = self.config.end_header_id_template
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system_message_template = self.config.system_message_template
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choices = "\n".join([f"{i}. {possible_answer}" for i, possible_answer in enumerate(possible_answers)])
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elements = [conditionning] if conditionning!="" else []
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elements += [
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f"{start_header_id_template}{system_message_template}{end_header_id_template}",
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"Answer this multi choices question.",
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"Answer with an id from the possible answers.",
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"Do not answer with an id outside this possible answers.",
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]
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if context!="":
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elements+=[
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f"{start_header_id_template}context{end_header_id_template}",
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self.system_custom_header("context"),
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f"{context}",
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]
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elements += [
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f"{start_header_id_template}question{end_header_id_template}{question}",
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f"{start_header_id_template}possible answers{end_header_id_template}",
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"Answer this multi choices question about the context:\n",
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]
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elements += [
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self.system_custom_header("question"),
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question,
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self.system_custom_header("possible answers"),
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f"{choices}",
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]
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elements += [f"{start_header_id_template}answer{end_header_id_template}"]
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prompt = self.build_prompt(elements)
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gen = self.generate(prompt, max_answer_length, temperature=0.1, top_k=50, top_p=0.9, repeat_penalty=1.0, repeat_last_n=50, callback=self.sink).strip().replace("</s>","").replace("<s>","")
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selection = gen.strip().split()[0].replace(",","").replace(".","")
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self.print_prompt("Multi choice selection",prompt+gen)
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code = self.generate_code(
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prompt,
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self.image_files,"""{
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"choice_index": [an int representing the index of the choice made]
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"justification": "[Justify the choice]",
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}""",
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max_size= max_answer_length,
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temperature=0.1,
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top_k=50,
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top_p=0.9,
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repeat_penalty=1.0,
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repeat_last_n=50,
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callback=self.sink,
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accept_all_if_no_code_tags_is_present=True
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)
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try:
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if not return_explanation:
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return int(selection)
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if len(code)>0:
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json_code = json.loads(code)
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selection = json_code["choice_index"]
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self.print_prompt("Multi choice selection",prompt+code)
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try:
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if return_explanation:
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return int(selection), json_code["justification"]
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else:
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return int(selection)
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except:
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ASCIIColors.cyan("Model failed to answer the question")
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return -1
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else:
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return int(selection), gen
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except:
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ASCIIColors.cyan("Model failed to answer the question")
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return -1
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return -1
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except Exception as ex:
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if return_explanation:
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return int(code.split('\n')[0]), ""
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else:
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return int(code.split('\n')[0])
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def multichoice_ranking(self, question: str, possible_answers:list, context:str = "", max_answer_length: int = 50, conditionning="") -> int:
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"""
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Ranks answers for a question from best to worst. returns a list of integers
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@ -2392,16 +2408,20 @@ Don't forget to close the html code tag.
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@property
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def ai_full_header(self) -> str:
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"""Get the start_header_id_template."""
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return f"{self.start_user_header_id_template}{self.name}{self.end_user_header_id_template}"
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return f"{self.start_ai_header_id_template}{self.name}{self.end_ai_header_id_template}"
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def system_custom_header(self, ai_name) -> str:
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"""Get the start_header_id_template."""
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return f"{self.start_user_header_id_template}{ai_name}{self.end_user_header_id_template}"
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def ai_custom_header(self, ai_name) -> str:
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def user_custom_header(self, ai_name) -> str:
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"""Get the start_header_id_template."""
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return f"{self.start_user_header_id_template}{ai_name}{self.end_user_header_id_template}"
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def ai_custom_header(self, ai_name) -> str:
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"""Get the start_header_id_template."""
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return f"{self.start_ai_header_id_template}{ai_name}{self.end_ai_header_id_template}"
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# ========================================== Helper methods ==========================================
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def detect_antiprompt(self, text:str) -> bool:
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@ -4495,7 +4515,7 @@ transition-all duration-300 ease-in-out">
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Returns:
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bool: True if the user prompt is asking to generate an image, False otherwise.
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"""
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return self.multichoice_question(question, ["no","yes"], context, max_answer_length, conditionning=conditionning)>0
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return self.personality.multichoice_question(question, ["no","yes"], context, max_answer_length, conditionning=conditionning)>0
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def multichoice_question(self, question: str, possible_answers:list, context:str = "", max_answer_length: int = 1024, conditionning="", return_justification=False) -> int:
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"""
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@ -4510,52 +4530,7 @@ transition-all duration-300 ease-in-out">
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Returns:
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int: Index of the selected option within the possible_ansers list. Or -1 if there was not match found among any of them.
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"""
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choices = "\n".join([f"{i}. {possible_answer}" for i, possible_answer in enumerate(possible_answers)])
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elements = [conditionning] if conditionning!="" else []
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if context!="":
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elements+=[
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self.system_custom_header("context"),
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f"{context}",
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]
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elements += [
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"Answer this multi choices question about the context:\n",
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]
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elements += [
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self.system_custom_header("question"),
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question,
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self.system_custom_header("possible answers"),
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f"{choices}",
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]
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prompt = self.build_prompt(elements)
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code = self.generate_code(
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prompt,
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self.personality.image_files,"""{
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"choice_index": [an int representing the index of the choice made]
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"justification": "[Justify the choice]",
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}""",
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max_size= max_answer_length,
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temperature=0.1,
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top_k=50,
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top_p=0.9,
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repeat_penalty=1.0,
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repeat_last_n=50,
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callback=self.sink
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)
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if len(code)>0:
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json_code = json.loads(code)
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selection = json_code["choice_index"]
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self.print_prompt("Multi choice selection",prompt+code)
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try:
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if return_justification:
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return int(selection), json_code["justification"]
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else:
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return int(selection)
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except:
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ASCIIColors.cyan("Model failed to answer the question")
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return -1
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else:
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return -1
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self.personality.multichoice_question(question, possible_answers, context, max_answer_length, conditionning, return_justification)
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def multichoice_ranking(self, question: str, possible_answers:list, context:str = "", max_answer_length: int = 50, conditionning="") -> int:
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"""
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