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app.py

@@ -1,198 +1,211 @@
-# Importing required libraries
-import warnings
-warnings.filterwarnings("ignore")
-
-import json
-import subprocess
-from llama_cpp import Llama
-from llama_cpp_agent import LlamaCppAgent
-from llama_cpp_agent import MessagesFormatterType
-from llama_cpp_agent.providers import LlamaCppPythonProvider
-from llama_cpp_agent.chat_history import BasicChatHistory
-from llama_cpp_agent.chat_history.messages import Roles
-import gradio as gr
-from huggingface_hub import hf_hub_download
-
-
-# Download gguf model files
-llm = None
-llm_model = None
-
-hf_hub_download(
-    repo_id="bartowski/Dolphin3.0-Llama3.2-1B-GGUF",
-    filename="Dolphin3.0-Llama3.2-1B-Q6_K.gguf",
-    local_dir="./models",
-)
-hf_hub_download(
-    repo_id="bartowski/Dolphin3.0-Llama3.2-3B-GGUF",
-    filename="Dolphin3.0-Llama3.2-3B-Q4_K_M.gguf",
-    local_dir="./models",
-)
-
-# Set the title and description
-title = "Dolphin🐬 Llamacpp"
-description = """Dolphin 3.0 is a powerful, general-purpose local AI model designed for coding, math, and various other tasks, aiming similar to the models like ChatGPT and Claude."""
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    model,
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    top_k,
-    repeat_penalty,
-):
-    """
-    Respond to a message using the Dolphin-3 model via Llama.cpp.
-
-    Args:
-        - message (str): The message to respond to.
-        - history (list[tuple[str, str]]): The chat history.
-        - model (str): The model to use.
-        - system_message (str): The system message to use.
-        - max_tokens (int): The maximum number of tokens to generate.
-        - temperature (float): The temperature of the model.
-        - top_p (float): The top-p of the model.
-        - top_k (int): The top-k of the model.
-        - repeat_penalty (float): The repetition penalty of the model.
-
-    Returns:
-        str: The response to the message.
-    """
-    # Load the global variables
-    global llm
-    global llm_model
-
-    # Load the model
-    if llm is None or llm_model != model:
-        llm = Llama(
-            model_path=f"models/{model}",
-            flash_attn=False,
-            n_gpu_layers=0,
-            n_batch=32,
-            n_ctx=8192,
-        )
-        llm_model = model
-    provider = LlamaCppPythonProvider(llm)
-
-    # Create the agent
-    agent = LlamaCppAgent(
-        provider,
-        system_prompt=f"{system_message}",
-        predefined_messages_formatter_type=MessagesFormatterType.CHATML,
-        debug_output=True,
-    )
-
-    # Set the settings like temperature, top-k, top-p, max tokens, etc.
-    settings = provider.get_provider_default_settings()
-    settings.temperature = temperature
-    settings.top_k = top_k
-    settings.top_p = top_p
-    settings.max_tokens = max_tokens
-    settings.repeat_penalty = repeat_penalty
-    settings.stream = True
-
-    messages = BasicChatHistory()
-
-    # Add the chat history
-    for msn in history:
-        user = {"role": Roles.user, "content": msn[0]}
-        assistant = {"role": Roles.assistant, "content": msn[1]}
-        messages.add_message(user)
-        messages.add_message(assistant)
-
-    # Get the response stream
-    stream = agent.get_chat_response(
-        message,
-        llm_sampling_settings=settings,
-        chat_history=messages,
-        returns_streaming_generator=True,
-        print_output=False,
-    )
-
-    # Generate the response
-    outputs = ""
-    for output in stream:
-        outputs += output
-        yield outputs
-
-
-# Create a chat interface
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs_accordion=gr.Accordion(
-        label="⚙️ Parameters", open=False, render=False
-    ),
-    additional_inputs=[
-        gr.Dropdown(
-            choices=[
-                "Dolphin3.0-Llama3.2-1B-Q6_K.gguf",
-                "Dolphin3.0-Llama3.2-3B-Q4_K_M.gguf",
-            ],
-            value="Dolphin3.0-Llama3.2-1B-Q6_K.gguf",
-            label="Model",
-            info="Select the AI model to use for chat"
-        ),
-        gr.Textbox(
-            value="You are Dolphin, a helpful AI assistant focused on accurate and ethical responses.",
-            label="System Prompt",
-            info="Define the AI assistant's personality and behavior",
-            lines=2
-        ),
-        gr.Slider(
-            minimum=512,
-            maximum=4096,
-            value=2048,
-            step=512,
-            label="Max Tokens",
-            info="Maximum length of response (higher = longer replies)"
-        ),
-        gr.Slider(
-            minimum=0.1,
-            maximum=2.0,
-            value=0.7,
-            step=0.1,
-            label="Temperature",
-            info="Creativity level (higher = more creative, lower = more focused)"
-        ),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p",
-            info="Nucleus sampling threshold"
-        ),
-        gr.Slider(
-            minimum=1,
-            maximum=100,
-            value=40,
-            step=1,
-            label="Top-k",
-            info="Limit vocabulary choices to top K tokens"
-        ),
-        gr.Slider(
-            minimum=1.0,
-            maximum=2.0,
-            value=1.1,
-            step=0.1,
-            label="Repetition Penalty",
-            info="Penalize repeated words (higher = less repetition)"
-        )
-    ],
-    theme="Ocean",
-    submit_btn="Send",
-    stop_btn="Stop",
-    title=title,
-    description=description,
-    chatbot=gr.Chatbot(scale=1, show_copy_button=True),
-    flagging_mode="never",
-)
-
-
-# Launch the chat interface
-if __name__ == "__main__":
-    demo.launch(debug=False)
+# Importing required libraries
+import warnings
+warnings.filterwarnings("ignore")
+
+import json
+import subprocess
+import sys
+from llama_cpp import Llama
+from llama_cpp_agent import LlamaCppAgent
+from llama_cpp_agent import MessagesFormatterType
+from llama_cpp_agent.providers import LlamaCppPythonProvider
+from llama_cpp_agent.chat_history import BasicChatHistory
+from llama_cpp_agent.chat_history.messages import Roles
+import gradio as gr
+from huggingface_hub import hf_hub_download
+from typing import List, Tuple
+from logger import logging
+from exception import CustomExceptionHandling
+
+
+# Download gguf model files
+llm = None
+llm_model = None
+
+hf_hub_download(
+    repo_id="bartowski/Dolphin3.0-Llama3.2-1B-GGUF",
+    filename="Dolphin3.0-Llama3.2-1B-Q6_K.gguf",
+    local_dir="./models",
+)
+hf_hub_download(
+    repo_id="bartowski/Dolphin3.0-Llama3.2-3B-GGUF",
+    filename="Dolphin3.0-Llama3.2-3B-Q4_K_M.gguf",
+    local_dir="./models",
+)
+
+# Set the title and description
+title = "Dolphin🐬 Llamacpp"
+description = """Dolphin 3.0 is a powerful, general-purpose local AI model designed for coding, math, and various other tasks, aiming similar to the models like ChatGPT and Claude."""
+
+
+def respond(
+    message: str,
+    history: List[Tuple[str, str]],
+    model: str,
+    system_message: str,
+    max_tokens: int,
+    temperature: float,
+    top_p: float,
+    top_k: int,
+    repeat_penalty: float,
+):
+    """
+    Respond to a message using the Dolphin-3 model via Llama.cpp.
+
+    Args:
+        - message (str): The message to respond to.
+        - history (List[Tuple[str, str]]): The chat history.
+        - model (str): The model to use.
+        - system_message (str): The system message to use.
+        - max_tokens (int): The maximum number of tokens to generate.
+        - temperature (float): The temperature of the model.
+        - top_p (float): The top-p of the model.
+        - top_k (int): The top-k of the model.
+        - repeat_penalty (float): The repetition penalty of the model.
+
+    Returns:
+        str: The response to the message.
+    """
+    try:
+        # Load the global variables
+        global llm
+        global llm_model
+
+        # Load the model
+        if llm is None or llm_model != model:
+            llm = Llama(
+                model_path=f"models/{model}",
+                flash_attn=False,
+                n_gpu_layers=0,
+                n_batch=32,
+                n_ctx=8192,
+            )
+            llm_model = model
+        provider = LlamaCppPythonProvider(llm)
+
+        # Create the agent
+        agent = LlamaCppAgent(
+            provider,
+            system_prompt=f"{system_message}",
+            predefined_messages_formatter_type=MessagesFormatterType.CHATML,
+            debug_output=True,
+        )
+
+        # Set the settings like temperature, top-k, top-p, max tokens, etc.
+        settings = provider.get_provider_default_settings()
+        settings.temperature = temperature
+        settings.top_k = top_k
+        settings.top_p = top_p
+        settings.max_tokens = max_tokens
+        settings.repeat_penalty = repeat_penalty
+        settings.stream = True
+
+        messages = BasicChatHistory()
+
+        # Add the chat history
+        for msn in history:
+            user = {"role": Roles.user, "content": msn[0]}
+            assistant = {"role": Roles.assistant, "content": msn[1]}
+            messages.add_message(user)
+            messages.add_message(assistant)
+
+        # Get the response stream
+        stream = agent.get_chat_response(
+            message,
+            llm_sampling_settings=settings,
+            chat_history=messages,
+            returns_streaming_generator=True,
+            print_output=False,
+        )
+
+        # Log the success
+        logging.info("Response stream generated successfully")
+
+        # Generate the response
+        outputs = ""
+        for output in stream:
+            outputs += output
+            yield outputs
+
+    # Handle exceptions that may occur during the process
+    except Exception as e:
+        # Custom exception handling
+        raise CustomExceptionHandling(e, sys) from e
+
+
+# Create a chat interface
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs_accordion=gr.Accordion(
+        label="⚙️ Parameters", open=False, render=False
+    ),
+    additional_inputs=[
+        gr.Dropdown(
+            choices=[
+                "Dolphin3.0-Llama3.2-1B-Q6_K.gguf",
+                "Dolphin3.0-Llama3.2-3B-Q4_K_M.gguf",
+            ],
+            value="Dolphin3.0-Llama3.2-1B-Q6_K.gguf",
+            label="Model",
+            info="Select the AI model to use for chat",
+        ),
+        gr.Textbox(
+            value="You are Dolphin, a helpful AI assistant focused on accurate and ethical responses.",
+            label="System Prompt",
+            info="Define the AI assistant's personality and behavior",
+            lines=2,
+        ),
+        gr.Slider(
+            minimum=512,
+            maximum=4096,
+            value=2048,
+            step=512,
+            label="Max Tokens",
+            info="Maximum length of response (higher = longer replies)",
+        ),
+        gr.Slider(
+            minimum=0.1,
+            maximum=2.0,
+            value=0.7,
+            step=0.1,
+            label="Temperature",
+            info="Creativity level (higher = more creative, lower = more focused)",
+        ),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p",
+            info="Nucleus sampling threshold",
+        ),
+        gr.Slider(
+            minimum=1,
+            maximum=100,
+            value=40,
+            step=1,
+            label="Top-k",
+            info="Limit vocabulary choices to top K tokens",
+        ),
+        gr.Slider(
+            minimum=1.0,
+            maximum=2.0,
+            value=1.1,
+            step=0.1,
+            label="Repetition Penalty",
+            info="Penalize repeated words (higher = less repetition)",
+        ),
+    ],
+    theme="Ocean",
+    submit_btn="Send",
+    stop_btn="Stop",
+    title=title,
+    description=description,
+    chatbot=gr.Chatbot(scale=1, show_copy_button=True),
+    flagging_mode="never",
+)
+
+
+# Launch the chat interface
+if __name__ == "__main__":
+    demo.launch(debug=False)

exception.py

@@ -0,0 +1,50 @@
+"""
+This module defines a custom exception handling class and a function to get error message with details of the error.
+"""
+
+# Standard Library
+import sys
+
+# Local imports
+from logger import logging
+
+
+# Function Definition to get error message with details of the error (file name and line number) when an error occurs in the program
+def get_error_message(error, error_detail: sys):
+    """
+    Get error message with details of the error.
+
+    Args:
+        - error (Exception): The error that occurred.
+        - error_detail (sys): The details of the error.
+
+    Returns:
+        str: A string containing the error message along with the file name and line number where the error occurred.
+    """
+    _, _, exc_tb = error_detail.exc_info()
+
+    # Get error details
+    file_name = exc_tb.tb_frame.f_code.co_filename
+    return "Error occured in python script name [{0}] line number [{1}] error message[{2}]".format(
+        file_name, exc_tb.tb_lineno, str(error)
+    )
+
+
+# Custom Exception Handling Class Definition
+class CustomExceptionHandling(Exception):
+    """
+    Custom Exception Handling:
+        This class defines a custom exception that can be raised when an error occurs in the program.
+        It takes an error message and an error detail as input and returns a formatted error message when the exception is raised.
+    """
+
+    # Constructor
+    def __init__(self, error_message, error_detail: sys):
+        """Initialize the exception"""
+        super().__init__(error_message)
+
+        self.error_message = get_error_message(error_message, error_detail=error_detail)
+
+    def __str__(self):
+        """String representation of the exception"""
+        return self.error_message

logger.py

@@ -0,0 +1,21 @@
+# Importing the required modules
+import os
+import logging
+from datetime import datetime
+
+# Creating a log file with the current date and time as the name of the file
+LOG_FILE = f"{datetime.now().strftime('%m_%d_%Y_%H_%M_%S')}.log"
+
+# Creating a logs folder if it does not exist
+logs_path = os.path.join(os.getcwd(), "logs", LOG_FILE)
+os.makedirs(logs_path, exist_ok=True)
+
+# Setting the log file path and the log level
+LOG_FILE_PATH = os.path.join(logs_path, LOG_FILE)
+
+# Configuring the logger
+logging.basicConfig(
+    filename=LOG_FILE_PATH,
+    format="[ %(asctime)s ] %(lineno)d %(name)s - %(levelname)s - %(message)s",
+    level=logging.INFO,
+)