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https://github.com/ParisNeo/lollms.git
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added new options
This commit is contained in:
Saifeddine ALOUI
parent
44516ab445
commit
f6c9019d27
@ -2259,6 +2259,83 @@ class APScript(StateMachine):
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"""
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pass
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def generate_html_from_dict(self, data):
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css_style = """
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<style>
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.container {
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font-family: 'Arial', sans-serif;
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max-width: 1000px;
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margin: 20px auto;
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padding: 20px;
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background-color: #f8f9fa;
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border-radius: 8px;
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box-shadow: 0 2px 4px rgba(0,0,0,0.1);
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}
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.header {
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background-color: #343a40;
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color: white;
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padding: 20px;
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border-radius: 6px;
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margin-bottom: 20px;
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}
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.section {
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margin-bottom: 20px;
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padding: 15px;
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background-color: white;
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border-radius: 6px;
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box-shadow: 0 1px 3px rgba(0,0,0,0.05);
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}
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.key {
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font-weight: bold;
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color: #495057;
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margin-bottom: 5px;
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}
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.value {
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color: #212529;
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white-space: pre-wrap;
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line-height: 1.6;
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}
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.list {
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margin: 0;
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padding-left: 20px;
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}
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</style>
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"""
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def format_value(value):
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if isinstance(value, list):
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if not value:
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return "<em>Empty list</em>"
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items = [f"<li>{item}</li>" for item in value]
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return f"<ul class='list'>{''.join(items)}</ul>"
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elif isinstance(value, (str, int, float)):
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# Replace pipe character and preserve formatting for multiline strings
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if isinstance(value, str) and '|' in value:
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value = value.replace('|', '').strip()
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return f"<div class='value'>{value}</div>"
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else:
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return f"<div class='value'>{str(value)}</div>"
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html = css_style
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html += "<div class='container'>"
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# Add header with name if it exists
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if "name" in data:
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html += f"<div class='header'><h1>{data['name']}</h1></div>"
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# Process all key-value pairs
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for key, value in data.items():
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if key != "name": # Skip name as it's already in header
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html += "<div class='section'>"
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html += f"<div class='key'>{key.replace('_', ' ').title()}:</div>"
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html += format_value(value)
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html += "</div>"
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html += "</div>"
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return html
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def execute_command(self, command: str, parameters:list=[], client:Client=None):
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"""
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Recovers user commands and executes them. Each personality can define a set of commands that they can receive and execute
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@ -2408,16 +2485,22 @@ class APScript(StateMachine):
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codes = self.extract_code_blocks(response)
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return codes
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def generate_code(self, prompt, images=[], max_size = None, temperature = None, top_k = None, top_p=None, repeat_penalty=None, repeat_last_n=None, callback=None, debug=False, return_full_generated_code=False ):
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def generate_code(self, prompt, images=[], max_size = None, temperature = None, top_k = None, top_p=None, repeat_penalty=None, repeat_last_n=None, callback=None, debug=False, return_full_generated_code=False, accept_all_if_no_code_tags_is_present=False):
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response_full = ""
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full_prompt = self.system_custom_header("Generation infos")+ "Generated code must be put inside the adequate markdown code tag. Use this template:\n```language name\nCode\n```\nMake sure only a single code tag is generated at each dialogue turn." + self.separator_template + self.system_custom_header("User prompt")+ prompt + self.separator_template + self.ai_custom_header("generated code")
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if len(self.personality.image_files)>0:
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response = self.personality.generate_with_images(self.system_custom_header("Generation infos")+ "Generated code must be put inside the adequate markdown code tag. Use this template:\n```language name\nCode\n```\nMake sure only a single code tag is generated at each dialogue turn." + self.separator_template + prompt, self.personality.image_files, max_size, temperature, top_k, top_p, repeat_penalty, repeat_last_n, callback, debug=debug)
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response = self.personality.generate_with_images(full_prompt, self.personality.image_files, max_size, temperature, top_k, top_p, repeat_penalty, repeat_last_n, callback, debug=debug)
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elif len(images)>0:
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response = self.personality.generate_with_images(self.system_custom_header("Generation infos")+ "Generated code must be put inside the adequate markdown code tag. Use this template:\n```language name\nCode\n```\nMake sure only a single code tag is generated at each dialogue turn." + self.separator_template + prompt, images, max_size, temperature, top_k, top_p, repeat_penalty, repeat_last_n, callback, debug=debug)
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response = self.personality.generate_with_images(full_prompt, images, max_size, temperature, top_k, top_p, repeat_penalty, repeat_last_n, callback, debug=debug)
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else:
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response = self.personality.generate(self.system_custom_header("Generation infos")+ "Generated code must be put inside the adequate markdown code tag. Use this template:\n```language name\nCode\n```\nMake sure only a single code tag is generated at each dialogue turn." + self.separator_template + prompt, max_size, temperature, top_k, top_p, repeat_penalty, repeat_last_n, callback, debug=debug)
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response = self.personality.generate(full_prompt, max_size, temperature, top_k, top_p, repeat_penalty, repeat_last_n, callback, debug=debug)
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response_full += response
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codes = self.extract_code_blocks(response)
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if len(codes)==0 and accept_all_if_no_code_tags_is_present:
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if return_full_generated_code:
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return response, response_full
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else:
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return response
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if len(codes)>0:
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if not codes[-1]["is_complete"]:
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code = "\n".join(codes[-1]["content"].split("\n")[:-1])
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@ -2441,6 +2524,124 @@ class APScript(StateMachine):
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return code
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else:
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return None
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def generate_text(self, prompt, images=[], max_size = None, temperature = None, top_k = None, top_p=None, repeat_penalty=None, repeat_last_n=None, callback=None, debug=False, return_full_generated_code=False, accept_all_if_no_code_tags_is_present=False):
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response_full = ""
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full_prompt = self.system_custom_header("Generation infos")+ "Generated text content must be put inside a markdown code tag. Use this template:\n```\nText\n```\nMake sure only a single text tag is generated at each dialogue turn." + self.separator_template + self.system_custom_header("User prompt")+ prompt + self.separator_template + self.ai_custom_header("generated answer")
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if len(self.personality.image_files)>0:
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response = self.personality.generate_with_images(full_prompt, self.personality.image_files, max_size, temperature, top_k, top_p, repeat_penalty, repeat_last_n, callback, debug=debug)
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elif len(images)>0:
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response = self.personality.generate_with_images(full_prompt, images, max_size, temperature, top_k, top_p, repeat_penalty, repeat_last_n, callback, debug=debug)
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else:
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response = self.personality.generate(full_prompt, max_size, temperature, top_k, top_p, repeat_penalty, repeat_last_n, callback, debug=debug)
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response_full += response
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codes = self.extract_code_blocks(response)
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if len(codes)==0 and accept_all_if_no_code_tags_is_present:
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if return_full_generated_code:
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return response, response_full
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else:
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return response
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if len(codes)>0:
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if not codes[-1]["is_complete"]:
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code = "\n".join(codes[-1]["content"].split("\n")[:-1])
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while not codes[-1]["is_complete"]:
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response = self.personality.generate(prompt+code+self.user_full_header+"continue the text. Start from last line and continue the text. Put the text inside a markdown code tag."+self.separator_template+self.ai_full_header, max_size, temperature, top_k, top_p, repeat_penalty, repeat_last_n, callback, debug=debug)
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response_full += response
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codes = self.extract_code_blocks(response)
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if len(codes)==0:
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break
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else:
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if not codes[-1]["is_complete"]:
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code +="\n"+ "\n".join(codes[-1]["content"].split("\n")[:-1])
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else:
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code +="\n"+ "\n".join(codes[-1]["content"].split("\n"))
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else:
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code = codes[-1]["content"]
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if return_full_generated_code:
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return code, response_full
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else:
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return code
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else:
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return None
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def generate_structured_content(self,
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prompt,
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template,
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single_shot=False,
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output_format="yaml"):
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"""
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Generate structured content (YAML/JSON) either in single-shot or step-by-step mode.
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Args:
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prompt (str): The main prompt describing what to generate
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template (dict): Dictionary containing the structure and field-specific prompts
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single_shot (bool): If True, generates all content at once. If False, generates field by field
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output_format (str): "yaml" or "json"
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Returns:
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dict: Contains both the structured data and formatted string
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"""
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# Initialize the output dictionary with default values
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output_data = {}
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for field, field_info in template.items():
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output_data[field] = field_info.get("default", "")
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if single_shot:
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# Generate all content at once for powerful LLMs
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full_prompt = f"""Generate {output_format.upper()} content for: {prompt}
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Use this structure:
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{output_data}
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"""
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response = self.generate_code(full_prompt, callback=self.sink, accept_all_if_no_code_tags_is_present=True)
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# Parse the response based on format
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if output_format == "yaml":
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import yaml
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try:
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cleaned_response = response.replace("```yaml", "").replace("```", "").strip()
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output_data = yaml.safe_load(cleaned_response)
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except yaml.YAMLError:
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# If parsing fails, fall back to step-by-step
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single_shot = False
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elif output_format == "json":
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import json
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try:
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cleaned_response = response.replace("```json", "").replace("```", "").strip()
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output_data = json.loads(cleaned_response)
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except json.JSONDecodeError:
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# If parsing fails, fall back to step-by-step
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single_shot = False
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if not single_shot:
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# Generate each field individually
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for field, field_info in template.items():
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if "prompt" in field_info:
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field_prompt = field_info["prompt"].format(main_prompt=prompt)
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response = self.generate_code(field_prompt, callback=self.sink, accept_all_if_no_code_tags_is_present=True )
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# Clean up the response
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cleaned_response = response.strip()
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# Apply any field-specific processing
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if "processor" in field_info:
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cleaned_response = field_info["processor"](cleaned_response)
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output_data[field] = cleaned_response
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# Format the output string
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if output_format == "yaml":
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formatted_string = ""
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for key, value in output_data.items():
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if isinstance(value, str) and ("\n" in value or len(value) > 40):
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v = value.replace('\n', '\n ')
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formatted_string += f"{key}: |\n {v}\n"
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else:
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formatted_string += f"{key}: {value}\n"
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else: # json
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formatted_string = json.dumps(output_data, indent=2)
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return {
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"data": output_data,
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"formatted_string": formatted_string
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}
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def run_workflow(self, prompt:str, previous_discussion_text:str="", callback: Callable[[str | list | None, MSG_OPERATION_TYPE, str, AIPersonality| None], bool]=None, context_details:dict=None, client:Client=None):
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"""
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