enhanced functions

lollms/functions/analyze_code/__init__.py

@@ -0,0 +1,7 @@
+from lollms.functions.analyze_code.create_project_database import create_project_database, create_project_database_function
+from lollms.functions.analyze_code.retreive_information_for_task import retrieve_information_for_task, retrieve_information_for_task_function
+from lollms.functions.analyze_code.retrieve_classes_from_project import retrieve_classes_from_project, retrieve_classes_from_project_function
+from lollms.functions.analyze_code.update_class_in_file import update_class_in_file, update_class_in_file_function
+from lollms.functions.analyze_code.update_function_in_file import update_function_in_file, update_function_in_file_function
+from lollms.functions.analyze_code.add_function_to_file import add_function_to_file, add_function_to_file_function
+

lollms/functions/analyze_code/add_function_to_file.py

@@ -0,0 +1,58 @@
+# Lollms function call definition file
+from functools import partial
+from lollms.utilities import PackageManager
+from ascii_colors import trace_exception
+from typing import List, Union
+from pathlib import Path
+import ast
+
+def add_function_to_file(file_path: Union[str, Path], function_name: str, function_content: str) -> str:
+    """
+    Adds a new function to the specified file.
+
+    Args:
+        file_path (Union[str, Path]): The path to the Python file.
+        function_name (str): The name of the function to add.
+        function_content (str): The content of the function to add.
+
+    Returns:
+        str: Success message or error message.
+    """
+    try:
+        file_path = Path(file_path)
+        
+        # Validate the file path
+        if not file_path.exists() or not file_path.is_file():
+            return "Invalid file path."
+        
+        with open(file_path, "r", encoding="utf-8") as file:
+            content = file.read()
+            tree = ast.parse(content)
+            
+            # Check if function already exists
+            for node in ast.walk(tree):
+                if isinstance(node, ast.FunctionDef) and node.name == function_name:
+                    return f"Function {function_name} already exists in the file."
+            
+            # Add the new function content at the end of the file
+            with open(file_path, "a", encoding="utf-8") as file:
+                file.write("\n\n" + function_content)
+            
+            return f"Function {function_name} added successfully to file {file_path}."
+        
+    except Exception as e:
+        return trace_exception(e)
+    
+
+# Metadata function 
+def add_function_to_file_function():
+    return {
+        "function_name": "add_function_to_file", # The function name in string
+        "function": add_function_to_file, # The function to be called
+        "function_description": "Adds a new function to the specified file.", # Description of the function
+        "function_parameters": [ # The set of parameters
+            {"name": "file_path", "type": "str"},
+            {"name": "function_name", "type": "str"},
+            {"name": "function_content", "type": "str"},
+        ]
+    }

lollms/functions/analyze_code/create_project_database.py

@@ -1,4 +1,8 @@
-# Lollms function call definition file
+"""
+
+"""
+
+
 from functools import partial
 from typing import List, Dict, Union, Any
 from lollms.utilities import PackageManager
@@ -130,101 +134,3 @@ def create_project_database_function(project_path, llm):
         "function_description": "Creates a database containing structured information about a Python project.",
         "function_parameters": []
     }
-
-
-
-# Lollms function call definition file
-from functools import partial
-from typing import List, Dict
-from lollms.utilities import PackageManager
-from ascii_colors import trace_exception
-from pathlib import Path
-import sqlite3
-
-# Ensure required packages are installed
-if not PackageManager.check_package_installed("sqlite3"):
-    PackageManager.install_package("sqlite3")
-
-def retrieve_information_for_task(project_path: str, task_description: str, llm: APScript) -> Union[str, Dict[str, str]]:
-    """
-    Retrieves information from the database to perform a task given by the user.
-    
-    Args:
-        project_path (str): The path to the project directory.
-        task_description (str): The description of the task to perform.
-        llm (APScript): The language model instance for generating SQL queries.
-    
-    Returns:
-        Union[str, Dict[str, str]]: A string containing relevant information or an error message.
-    """
-    try:
-        db_path = Path(project_path) / "project_info.db"
-        
-        # Validate the database path
-        if not db_path.exists() or not db_path.is_file():
-            return "Invalid database path."
-
-        conn = sqlite3.connect(db_path)
-        cursor = conn.cursor()
-
-        # Retrieve the list of classes and their descriptions
-        cursor.execute("SELECT name, docstring FROM classes")
-        classes = cursor.fetchall()
-
-        # Format the classes into a string
-        classes_text = "\n".join([f"Class: {cls[0]}, Description: {cls[1]}" for cls in classes])
-
-        # Ask the LLM which classes are needed for the task
-        prompt = f"{llm.personality.config.start_header_id_template}{llm.personality.config.system_message_template}{llm.personality.config.end_header_id_template}" \
-                 f"Given the following list of classes and their descriptions:\n" \
-                 f"{classes_text}\n\n" \
-                 f"Task description: {task_description}\n\n" \
-                 f"{llm.personality.config.start_header_id_template}instructions{llm.personality.config.end_header_id_template}" \
-                 f"Which classes are needed to perform the task? List the class names.\n" \
-                 f"Answer in form of a json list inside a json markdown tag.\n" \
-                 f"{llm.personality.config.start_header_id_template}assistant{llm.personality.config.end_header_id_template}"
-
-        needed_classes = llm.fast_gen(prompt, callback=llm.sink).strip()
-        needed_classes = llm.extract_code_blocks(needed_classes)
-        if len(needed_classes)>0:
-            needed_classes = json.loads(needed_classes[0]["content"])
-            # Retrieve the relevant information for the needed classes
-            class_info = {}
-            for class_name in needed_classes:
-                cursor.execute("SELECT * FROM classes WHERE name = ?", (class_name,))
-                class_info[class_name] = cursor.fetchone()
-
-            # Retrieve the project description and structure
-            cursor.execute("SELECT name, description FROM project_info")
-            project_info = cursor.fetchone()
-
-            cursor.execute("SELECT name FROM sqlite_master WHERE type='table'")
-            tables = cursor.fetchall()
-
-            conn.close()
-
-            # Format the results into a string
-            result_text = f"Project Name: {project_info[0]}\nProject Description: {project_info[1]}\n\n"
-            result_text += "Project Structure:\n" + "\n".join([table[0] for table in tables]) + "\n\n"
-            result_text += "Needed Classes Information:\n"
-            for class_name, info in class_info.items():
-                result_text += f"Class: {class_name}\n"
-                result_text += f"Description: {info[2]}\n"
-                result_text += f"Methods: {info[4]}\n"
-                result_text += f"Static Methods: {info[5]}\n\n"
-
-            return result_text.strip()
-        else:
-            return "Failed to ask the llm"        
-    except Exception as e:
-        return str(e)
-    
-def retrieve_information_for_task_function(project_path, llm):
-    return {
-        "function_name": "retrieve_information_for_task",
-        "function": partial(retrieve_information_for_task, project_path=project_path, llm=llm),
-        "function_description": "Retrieves information from the database to perform a task given by the user.",
-        "function_parameters": [
-            {"name": "task_description", "type": "str", "description":"a description of "}
-        ]
-    }

lollms/functions/analyze_code/retreive_information_for_task.py

@@ -0,0 +1,96 @@
+# Lollms function call definition file
+from functools import partial
+from typing import List, Dict, Union
+from lollms.utilities import PackageManager
+from lollms.personality import APScript
+from ascii_colors import trace_exception
+from pathlib import Path
+import sqlite3
+import json 
+# Ensure required packages are installed
+if not PackageManager.check_package_installed("sqlite3"):
+    PackageManager.install_package("sqlite3")
+
+def retrieve_information_for_task(project_path: str, task_description: str, llm: APScript) -> Union[str, Dict[str, str]]:
+    """
+    Retrieves information from the database to perform a task given by the user.
+    
+    Args:
+        project_path (str): The path to the project directory.
+        task_description (str): The description of the task to perform.
+        llm (APScript): The language model instance for generating SQL queries.
+    
+    Returns:
+        Union[str, Dict[str, str]]: A string containing relevant information or an error message.
+    """
+    try:
+        db_path = Path(project_path) / "project_info.db"
+        
+        # Validate the database path
+        if not db_path.exists() or not db_path.is_file():
+            return "Invalid database path."
+
+        conn = sqlite3.connect(db_path)
+        cursor = conn.cursor()
+
+        # Retrieve the list of classes and their descriptions
+        cursor.execute("SELECT name, docstring FROM classes")
+        classes = cursor.fetchall()
+
+        # Format the classes into a string
+        classes_text = "\n".join([f"Class: {cls[0]}, Description: {cls[1]}" for cls in classes])
+
+        # Ask the LLM which classes are needed for the task
+        prompt = f"{llm.personality.config.start_header_id_template}{llm.personality.config.system_message_template}{llm.personality.config.end_header_id_template}" \
+                 f"Given the following list of classes and their descriptions:\n" \
+                 f"{classes_text}\n\n" \
+                 f"Task description: {task_description}\n\n" \
+                 f"{llm.personality.config.start_header_id_template}instructions{llm.personality.config.end_header_id_template}" \
+                 f"Which classes are needed to perform the task? List the class names.\n" \
+                 f"Answer in form of a json list inside a json markdown tag.\n" \
+                 f"{llm.personality.config.start_header_id_template}assistant{llm.personality.config.end_header_id_template}"
+
+        needed_classes = llm.fast_gen(prompt, callback=llm.sink).strip()
+        needed_classes = llm.extract_code_blocks(needed_classes)
+        if len(needed_classes)>0:
+            needed_classes = json.loads(needed_classes[0]["content"])
+            # Retrieve the relevant information for the needed classes
+            class_info = {}
+            for class_name in needed_classes:
+                cursor.execute("SELECT * FROM classes WHERE name = ?", (class_name,))
+                class_info[class_name] = cursor.fetchone()
+
+            # Retrieve the project description and structure
+            cursor.execute("SELECT name, description FROM project_info")
+            project_info = cursor.fetchone()
+
+            cursor.execute("SELECT name FROM sqlite_master WHERE type='table'")
+            tables = cursor.fetchall()
+
+            conn.close()
+
+            # Format the results into a string
+            result_text = f"Project Name: {project_info[0]}\nProject Description: {project_info[1]}\n\n"
+            result_text += "Project Structure:\n" + "\n".join([table[0] for table in tables]) + "\n\n"
+            result_text += "Needed Classes Information:\n"
+            for class_name, info in class_info.items():
+                result_text += f"Class: {class_name}\n"
+                result_text += f"Description: {info[2]}\n"
+                result_text += f"Methods: {info[4]}\n"
+                result_text += f"Static Methods: {info[5]}\n\n"
+
+            return result_text.strip()
+        else:
+            return "Failed to ask the llm"        
+    except Exception as e:
+        return str(e)
+    
+def retrieve_information_for_task_function(project_path, llm):
+    return {
+        "function_name": "retrieve_information_for_task",
+        "function": partial(retrieve_information_for_task, project_path=project_path, llm=llm),
+        "function_description": "Retrieves information from the database to perform a task given by the user.",
+        "function_parameters": [
+            {"name": "task_description", "type": "str", "description":"a description of "}
+        ]
+    }

lollms/functions/analyze_code/retrieve_classes_from_project.py

@@ -0,0 +1,59 @@
+# Import necessary libraries
+from pathlib import Path
+from functools import partial
+
+from typing import List, Union
+import ast
+
+# ascii_colors offers advanced console coloring and bug tracing
+from ascii_colors import trace_exception
+
+# Function to retrieve classes from the project files
+def retrieve_classes_from_project(class_names: List[str], project_path: Union[str, Path]) -> str:
+    """
+    Retrieves the code of specified classes from the given project path.
+
+    Args:
+        project_path (Union[str, Path]): The path to the Python project directory.
+        class_names (List[str]): List of class names to retrieve.
+
+    Returns:
+        str: The code of the specified classes as a string.
+    """
+    try:
+        project_path = Path(project_path)
+        
+        # Validate the project path
+        if not project_path.exists() or not project_path.is_dir():
+            return "Invalid project path."
+        
+        class_code = "\n"
+        
+        # Traverse the project directory and extract class code
+        for py_file in project_path.rglob("*.py"):
+            with open(py_file, "r", encoding="utf-8") as file:
+                content = file.read()
+                tree = ast.parse(content)
+                
+                for node in ast.walk(tree):
+                    if isinstance(node, ast.ClassDef) and node.name in class_names:
+                        class_code += f"\n\n# Class: {node.name} in file: {py_file.relative_to(project_path)}\n"
+                        class_code += "```python\n"
+                        class_code += "\n".join(content.split("\n")[node.lineno-1:node.end_lineno])
+                        class_code += "\n```\n"
+        
+        return class_code if class_code else "No specified classes found."
+        
+    except Exception as e:
+        return trace_exception(e)
+
+# Metadata function
+def retrieve_classes_from_project_function(project_path:str):
+    return {
+        "function_name": "retrieve_classes_from_project", 
+        "function": partial(retrieve_classes_from_project,project_path = project_path),
+        "function_description": "Retrieves the code of specified classes from the given project path.",
+        "function_parameters": [
+            {"name": "class_names", "type": "list", "description": "List of class names to retrieve."}
+        ]          
+    }

lollms/functions/analyze_code/update_class_in_file.py

@@ -0,0 +1,85 @@
+# Lollms function call definition file
+from functools import partial
+from lollms.utilities import PackageManager
+from ascii_colors import trace_exception
+from typing import List, Union
+from pathlib import Path
+import ast
+def update_class_in_file(class_name: str, new_content: str, method_name: Union[str, None] = None, project_path: Union[str, Path] = "") -> str:
+    """
+    Updates the specified class or method with new content in the given project path.
+
+    Args:
+        class_name (str): The name of the class to update.
+        new_content (str): The new content to replace the class or method with.
+        method_name (Union[str, None]): The name of the method to update. If None, updates the entire class.
+        project_path (Union[str, Path]): The path to the Python project directory.
+
+    Returns:
+        str: Success message or error message.
+    """
+    try:
+        project_path = Path(project_path)
+        
+        # Validate the project path
+        if not project_path.exists() or not project_path.is_dir():
+            return "Invalid project path."
+        
+        # Traverse the project directory and update class or method content
+        for py_file in project_path.rglob("*.py"):
+            with open(py_file, "r", encoding="utf-8") as file:
+                content = file.read()
+                tree = ast.parse(content)
+                
+                class_found = False
+                method_found = False
+                updated_content = []
+                lines = content.split("\n")
+                
+                for node in ast.walk(tree):
+                    if isinstance(node, ast.ClassDef) and node.name == class_name:
+                        class_found = True
+                        if method_name is None:
+                            # Update entire class content
+                            updated_content.extend(lines[:node.lineno-1])
+                            updated_content.append(new_content)
+                            updated_content.extend(lines[node.end_lineno:])
+                        else:
+                            # Update specific method content
+                            for class_node in node.body:
+                                if isinstance(class_node, ast.FunctionDef) and class_node.name == method_name:
+                                    method_found = True
+                                    # Calculate the indentation level
+                                    indent_level = len(lines[class_node.lineno-1]) - len(lines[class_node.lineno-1].lstrip())
+                                    indented_new_content = "\n".join(" " * indent_level + line for line in new_content.split("\n"))
+                                    updated_content.extend(lines[:class_node.lineno-1])
+                                    updated_content.append(indented_new_content)
+                                    updated_content.extend(lines[class_node.end_lineno:])
+                                    break
+                            if not method_found:
+                                return f"Method {method_name} not found in class {class_name}."
+                        break
+                
+                if class_found:
+                    with open(py_file, "w", encoding="utf-8") as file:
+                        file.write("\n".join(updated_content))
+                    return f"{'Method ' + method_name if method_name else 'Class ' + class_name} updated successfully in file {py_file.relative_to(project_path)}."
+        
+        return "Class not found in the specified project."
+        
+    except Exception as e:
+        return trace_exception(e)
+    
+
+# Metadata function 
+def update_class_in_file_function(project_path:str ):
+    return {
+        "function_name": "update_class_in_file", # The function name in string
+        "function": partial(update_class_in_file,project_path = project_path), # The function to be called
+        "function_description": "Updates the specified class with new content in the given project path.", # Description of the function
+        "function_parameters": [ # The set of parameters
+            {"name": "class_name", "type": "str"},
+            {"name": "new_content", "type": "str"},
+            {"name": "method_name", "type": "str", "description":"An optional method name, required only if you need to change a single method content. If you need to change the whole class, do not set this element"},
+        ]
+    }

lollms/functions/analyze_code/update_function_in_file.py

@@ -0,0 +1,70 @@
+# Lollms function call definition file
+from functools import partial
+from lollms.utilities import PackageManager
+from ascii_colors import trace_exception
+from typing import List, Union
+from pathlib import Path
+import ast
+
+def update_function_in_file(function_name: str, new_content: str, project_path: Union[str, Path] = "") -> str:
+    """
+    Updates the specified function with new content in the given project path.
+
+    Args:
+        function_name (str): The name of the function to update.
+        new_content (str): The new content to replace the function with.
+        project_path (Union[str, Path]): The path to the Python project directory.
+
+    Returns:
+        str: Success message or error message.
+    """
+    try:
+        project_path = Path(project_path)
+        
+        # Validate the project path
+        if not project_path.exists() or not project_path.is_dir():
+            return "Invalid project path."
+        
+        # Traverse the project directory and update function content
+        for py_file in project_path.rglob("*.py"):
+            with open(py_file, "r", encoding="utf-8") as file:
+                content = file.read()
+                tree = ast.parse(content)
+                
+                function_found = False
+                updated_content = []
+                lines = content.split("\n")
+                
+                for node in ast.walk(tree):
+                    if isinstance(node, ast.FunctionDef) and node.name == function_name:
+                        function_found = True
+                        # Calculate the indentation level
+                        indent_level = len(lines[node.lineno-1]) - len(lines[node.lineno-1].lstrip())
+                        indented_new_content = "\n".join(" " * indent_level + line for line in new_content.split("\n"))
+                        updated_content.extend(lines[:node.lineno-1])
+                        updated_content.append(indented_new_content)
+                        updated_content.extend(lines[node.end_lineno:])
+                        break
+                
+                if function_found:
+                    with open(py_file, "w", encoding="utf-8") as file:
+                        file.write("\n".join(updated_content))
+                    return f"Function {function_name} updated successfully in file {py_file.relative_to(project_path)}."
+        
+        return "Function not found in the specified project."
+        
+    except Exception as e:
+        return trace_exception(e)
+    
+    
+# Metadata function 
+def update_function_in_file_function(project_path:str ):
+    return {
+        "function_name": "update_function_in_file", # The function name in string
+        "function": partial(update_function_in_file, project_path=project_path), # The function to be called
+        "function_description": "Updates the specified function with new content in the given project path.", # Description of the function
+        "function_parameters": [ # The set of parameters
+            {"name": "function_name", "type": "str"},
+            {"name": "new_content", "type": "str"},
+        ]
+    }

lollms/functions/image_management/draw_bounding_boxes.py

@@ -0,0 +1,75 @@
+# Lollms function call definition file
+# File Name: draw_bounding_boxes.py
+# Author: Saif (ParisNeo)
+# Description: This script defines a function that takes an image file path and a list of bounding boxes in the form of x, y, w, h in normalized mode along with the labels, and returns the bounding boxes placed on top of the images with the labels.
+
+# Lollms function call definition file
+from functools import partial
+from typing import List, Tuple
+from lollms.utilities import PackageManager
+from ascii_colors import trace_exception
+from pathlib import Path
+
+# Ensure necessary packages are installed
+if not PackageManager.check_package_installed("Pillow"):
+    PackageManager.install_package("Pillow")
+
+from PIL import Image, ImageDraw, ImageFont
+
+def draw_bounding_boxes(image_path: str, bounding_boxes: List[Tuple[float, float, float, float, str]]) -> str:
+    """
+    Draws bounding boxes on an image and saves the result.
+    
+    Args:
+        image_path (str): Path to the input image file.
+        bounding_boxes (List[Tuple[float, float, float, float, str]]): List of bounding boxes in normalized coordinates (x, y, w, h) along with labels.
+        
+    Returns:
+        str: Path to the output image file with bounding boxes drawn.
+    """
+    try:
+        image_path = Path(image_path)
+        if not image_path.exists():
+            raise FileNotFoundError(f"Image file not found: {image_path}")
+
+        # Load the image
+        image = Image.open(image_path)
+        draw = ImageDraw.Draw(image)
+        width, height = image.size
+        
+        # Iterate over bounding boxes and draw them
+        for bbox in bounding_boxes:
+            x, y, w, h, label = bbox
+            left = x * width
+            top = y * height
+            right = left + (w * width)
+            bottom = top + (h * height)
+            
+            # Draw rectangle
+            draw.rectangle([left, top, right, bottom], outline="red", width=2)
+            
+            # Draw label
+            font = ImageFont.load_default()
+            text_size = draw.textsize(label, font=font)
+            text_background = [left, top - text_size[1], left + text_size[0], top]
+            draw.rectangle(text_background, fill="red")
+            draw.text((left, top - text_size[1]), label, fill="white", font=font)
+        
+        # Save the output image
+        output_path = image_path.with_name(f"{image_path.stem}_with_boxes{image_path.suffix}")
+        image.save(output_path)
+        
+        return str(output_path)
+    except Exception as e:
+        return trace_exception(e)
+
+def draw_bounding_boxes_function():
+    return {
+        "function_name": "draw_bounding_boxes",
+        "function": draw_bounding_boxes,
+        "function_description": "Draws bounding boxes on an image and saves the result.",
+        "function_parameters": [
+            {"name": "image_path", "type": "str"},
+            {"name": "bounding_boxes", "type": "List[Tuple[float, float, float, float, str]]"}
+        ]
+    }

lollms/functions/timers.py

@@ -1,4 +1,8 @@
 # Lollms function call definition file
+# File Name: set_timer_with_alert.py
+# Author: Saif (ParisNeo)
+# Description: This function sets a non-blocking timer that shows a PyQt window with a message and makes noise after a specified duration. It works on any operating system by using the pyautogui library for the alert sound.
+
 # Here you need to import any necessary imports depending on the function requested by the user
 # example import math
 
@@ -6,7 +10,7 @@
 from functools import partial
 
 # It is advised to import typing elements
-# from typing import List
+from typing import Any, Dict
 
 # Import PackageManager if there are potential libraries that need to be installed 
 from lollms.utilities import PackageManager
@@ -17,30 +21,35 @@ from ascii_colors import trace_exception
 # Here is an example of how we install a non installed library using PackageManager
 if not PackageManager.check_package_installed("PyQt5"):
     PackageManager.install_package("PyQt5")
+if not PackageManager.check_package_installed("pyautogui"):
+    PackageManager.install_package("pyautogui")
 
-# now we can import the library
+# now we can import the libraries
 import threading
 import time
 import sys
 from PyQt5.QtWidgets import QApplication, QMessageBox
-import winsound
+import pyautogui
 
 # here is the core of the function to be built
 def set_timer_with_alert(duration: int, message: str) -> str:
-    def timer_callback():
-        if type(duration)==str and len(duration)>0:
-            duration = float(duration.split()[0])
-        time.sleep(duration)
-        winsound.Beep(1000, 1000)  # Make noise when time is up
+    """
+    Sets a non-blocking timer that shows a PyQt window with a message and makes noise after a specified duration.
 
-        app = QApplication(sys.argv)
-        msg_box = QMessageBox()
-        msg_box.setIcon(QMessageBox.Information)
-        msg_box.setWindowTitle("Timer Alert")
-        msg_box.setText(message)
-        msg_box.setStandardButtons(QMessageBox.Ok)
-        msg_box.buttonClicked.connect(app.quit)
-        msg_box.exec_()
+    Parameters:
+    duration (int): The duration for the timer in seconds.
+    message (str): The message to be displayed in the alert window.
+
+    Returns:
+    str: A success message indicating the timer has been set.
+    """
+    def timer_callback():
+        try:
+            time.sleep(duration)
+            pyautogui.alert(text=message, title="Timer Alert", button='OK')
+            pyautogui.beep()
+        except Exception as e:
+            return trace_exception(e)
         
     try:
         # Start the timer in a new thread to make it non-blocking
@@ -53,7 +62,7 @@ def set_timer_with_alert(duration: int, message: str) -> str:
         return trace_exception(e)
 
 # Here is the metadata function that should have the name in format function_name_function
-def set_timer_with_alert_function(processor, client):
+def set_timer_with_alert_function() -> Dict[str, Any]:
     return {
         "function_name": "set_timer_with_alert", # The function name in string
         "function": set_timer_with_alert, # The function to be called

lollms/functions/tts/read_text.py

@@ -0,0 +1,45 @@
+# Lollms function call definition file
+
+# Import necessary modules
+from pathlib import Path
+from functools import partial
+from typing import Union
+from lollms.utilities import PackageManager
+from lollms.personality import APScript
+from lollms.tts import LollmsTTS
+from safe_store import GenericDataLoader
+from ascii_colors import trace_exception
+
+# Here is the core of the function to be built
+def read_text(text: str, tts_module:LollmsTTS, llm:APScript) -> str:
+    """
+    This function takes a TTS module and a file path as input, reads the text from the file,
+    and uses the TTS module to generate audio from the text.
+    
+    Parameters:
+    tts_module: The text-to-speech module with a method tts_audio.
+    text: The text to be read.
+
+    Returns:
+    str: The path to the generated audio file.
+    """
+    try:        
+        # Generate audio from the text
+        audio_file_path = tts_module.tts_audio(text)
+        llm.chunk(text)
+        llm.new_message("")
+        
+        # Return the path to the generated audio file
+        return str(audio_file_path)
+    except Exception as e:
+        return trace_exception(e)
+    
+
+# Metadata function
+def read_text_function(file_path:str,tts_module:LollmsTTS):
+    return {
+        "function_name": "read_text_from_file", # The function name in string
+        "function": partial(read_text, file_path=file_path, tts_module=tts_module), # The function to be called
+        "function_description": "Reads text from a file and uses a TTS module to generate audio from the text.", # Description of the function
+        "function_parameters": [] # The set of parameters          
+    }

lollms/functions/tts/read_text_from_file.py

@@ -0,0 +1,50 @@
+# Lollms function call definition file
+
+# Import necessary modules
+from pathlib import Path
+from functools import partial
+from typing import Union
+from lollms.utilities import PackageManager
+from lollms.personality import APScript
+from lollms.tts import LollmsTTS
+from safe_store import GenericDataLoader
+from ascii_colors import trace_exception
+
+# Here is the core of the function to be built
+def read_text_from_file(file_path: Union[Path, str], tts_module:LollmsTTS, llm:APScript) -> str:
+    """
+    This function takes a TTS module and a file path as input, reads the text from the file,
+    and uses the TTS module to generate audio from the text.
+    
+    Parameters:
+    tts_module: The text-to-speech module with a method tts_audio.
+    file_path: The path to the text file containing the text to be read.
+
+    Returns:
+    str: The path to the generated audio file.
+    """
+    try:
+        # Ensure file_path is of type Path
+        file_path = Path(file_path)
+        
+        # Read the text from the file
+        text = GenericDataLoader.read_file(file_path)
+        
+        # Generate audio from the text
+        audio_file_path = tts_module.tts_audio(text,use_threading=True)
+        llm.full(text)
+        
+        # Return the path to the generated audio file
+        return str(audio_file_path)
+    except Exception as e:
+        return trace_exception(e)
+    
+
+# Metadata function
+def read_text_from_file_function(file_path:str,tts_module:LollmsTTS, llm:APScript):
+    return {
+        "function_name": "read_text_from_file", # The function name in string
+        "function": partial(read_text_from_file, file_path=file_path, tts_module=tts_module, llm=llm), # The function to be called
+        "function_description": "Reads text from the current file and uses a TTS module to generate audio from the text.", # Description of the function
+        "function_parameters": [] # The set of parameters          
+    }

lollms/functions/web/__init__.py

@@ -0,0 +1 @@
+from lollms.functions.web.google_search import google_search, google_search_function

lollms/functions/web/google_search.py

@@ -0,0 +1,46 @@
+# Lollms function call definition file
+
+# Import necessary libraries
+from functools import partial
+from typing import List
+from lollms.utilities import PackageManager
+from ascii_colors import trace_exception
+
+# Ensure the webbrowser package is available
+if not PackageManager.check_package_installed("webbrowser"):
+    PackageManager.install_package("webbrowser")
+
+# Import the webbrowser library
+import webbrowser
+
+# Define the function to perform a Google search
+def google_search(query: str) -> str:
+    """
+    Perform a Google search using the default web browser.
+
+    Parameters:
+    - query (str): The search query.
+
+    Returns:
+    - str: A message indicating the search was performed.
+    """
+    try:
+        # Construct the Google search URL
+        search_url = f"https://www.google.com/search?q={query}"
+        
+        # Open the search URL in the default web browser
+        webbrowser.open(search_url)
+        
+        # Return a success message
+        return f"Performed Google search for query: {query}"
+    except Exception as e:
+        return trace_exception(e)
+
+# Define the metadata function
+def google_search_function():
+    return {
+        "function_name": "google_search",  # The function name in string
+        "function": google_search,  # The function to be called
+        "function_description": "Performs a Google search using the default web browser.",  # Description of the function
+        "function_parameters": [{"name": "query", "type": "str"}]  # The set of parameters
+    }

lollms/personality.py

@@ -3394,6 +3394,7 @@ The AI should respond in this format using data from actions_list:
                         result = function(**parameters)
                         results.append(result)
                 except TypeError as e:
+                    trace_exception(e)
                     # Handle cases where the function call fails due to incorrect parameters, etc.
                     results.append(f"Error calling {function_name}: {e}")
             else:
@@ -3521,6 +3522,20 @@ The AI should respond in this format using data from actions_list:
         return function_calls
 
 
+    def interact(
+                    self, 
+                    context_details, 
+                    callback = None
+                    ):
+        upgraded_prompt = self.build_prompt_from_context_details(context_details)
+        if len(self.personality.image_files)>0:
+            # Generate the initial text based on the upgraded prompt.
+            generated_text = self.fast_gen_with_images(upgraded_prompt, self.personality.image_files, callback=callback)
+        else:    
+            generated_text = self.fast_gen(upgraded_prompt, callback=callback)
+
+        return generated_text
+
     def interact_with_function_call(
                                         self, 
                                         context_details, 
@@ -3528,21 +3543,23 @@ The AI should respond in this format using data from actions_list:
                                         prompt_after_execution=True, 
                                         callback = None, 
                                         hide_function_call=False,
-                                        separate_output=False):
+                                        separate_output=False,
+                                        max_nested_function_calls=10):
         
         start_header_id_template    = self.config.start_header_id_template
         end_header_id_template      = self.config.end_header_id_template
         system_message_template     = self.config.system_message_template
         separator_template          = self.config.separator_template
 
-
-
         final_output = ""
         if len(self.personality.image_files)>0:
             out, function_calls = self.generate_with_function_calls_and_images(context_details, self.personality.image_files, function_definitions, callback=callback)
         else:
             out, function_calls = self.generate_with_function_calls(context_details, function_definitions, callback=callback)
-        if len(function_calls)>0:
+        nested_function_calls = 0
+        while len(function_calls)>0 and nested_function_calls<max_nested_function_calls:
+            nested_function_calls += 1
+            self.chunk("\n") 
             if hide_function_call:
                 self.full("") #Hide function 
 
@@ -3562,11 +3579,6 @@ The AI should respond in this format using data from actions_list:
                 else:
                     out, function_calls = self.generate_with_function_calls(context_details, function_definitions, callback=callback)
                 final_output += "\n" + out
-                if len(function_calls)>0:
-                    outputs = self.execute_function_calls(function_calls,function_definitions)
-                    final_output = "\n".join([str(o) if type(o)==str else str(o[0]) if (type(o)==tuple or type(0)==list) and len(o)>0 else "" for o in outputs])
-                    out += f"{separator_template}{start_header_id_template}function calls results{end_header_id_template}\n" + final_output
-                    context_details["discussion_messages"] +=out
         else:
             final_output = out
         return final_output