Create app.py

app.py

@@ -0,0 +1,198 @@
+# 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/Qwen2.5-Coder-1.5B-Instruct-GGUF",
+    filename="Qwen2.5-Coder-1.5B-Instruct-Q6_K.gguf",
+    local_dir="./models",
+)
+hf_hub_download(
+    repo_id="bartowski/Qwen2.5-Coder-3B-Instruct-GGUF",
+    filename="Qwen2.5-Coder-3B-Instruct-Q5_K_S.gguf",
+    local_dir="./models",
+)
+
+# Set the title and description
+title = "Qwen-Coder Llama.cpp"
+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=[
+                "Qwen2.5-Coder-1.5B-Instruct-Q6_K.gguf",
+                "Qwen2.5-Coder-3B-Instruct-Q5_K_S.gguf",
+            ],
+            value="Qwen2.5-Coder-1.5B-Instruct-Q6_K.gguf",
+            label="Model",
+            info="Select the AI model to use for chat",
+        ),
+        gr.Textbox(
+            value="You are Qwen, created by Alibaba Cloud. You are a helpful assistant.",
+            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)