Delete App_Function_Libraries/Gradio_UI/Llamafile_tab.py

App_Function_Libraries/Gradio_UI/Llamafile_tab.py

@@ -1,312 +0,0 @@
-# Llamafile_tab.py
-# Description: Gradio interface for configuring and launching Llamafile with Local LLMs
-
-# Imports
-import os
-import logging
-from typing import Tuple, Optional
-import gradio as gr
-
-
-from App_Function_Libraries.Local_LLM.Local_LLM_Inference_Engine_Lib import (
-    download_llm_model,
-    llm_models,
-    start_llamafile,
-    get_gguf_llamafile_files
-)
-#
-#######################################################################################################################
-#
-# Functions:
-
-def create_chat_with_llamafile_tab():
-    # Function to update model path based on selection
-    def on_local_model_change(selected_model: str, search_directory: str) -> str:
-        if selected_model and isinstance(search_directory, str):
-            model_path = os.path.abspath(os.path.join(search_directory, selected_model))
-            logging.debug(f"Selected model path: {model_path}")  # Debug print for selected model path
-            return model_path
-        return "Invalid selection or directory."
-
-    # Function to update the dropdown with available models
-    def update_dropdowns(search_directory: str) -> Tuple[dict, str]:
-        logging.debug(f"User-entered directory: {search_directory}")  # Debug print for directory
-        if not os.path.isdir(search_directory):
-            logging.debug(f"Directory does not exist: {search_directory}")  # Debug print for non-existing directory
-            return gr.update(choices=[], value=None), "Directory does not exist."
-
-        logging.debug(f"Directory exists: {search_directory}, scanning for files...")  # Confirm directory exists
-        model_files = get_gguf_llamafile_files(search_directory)
-
-        if not model_files:
-            logging.debug(f"No model files found in {search_directory}")  # Debug print for no files found
-            return gr.update(choices=[], value=None), "No model files found in the specified directory."
-
-        # Update the dropdown choices with the model files found
-        logging.debug(f"Models loaded from {search_directory}: {model_files}")  # Debug: Print model files loaded
-        return gr.update(choices=model_files, value=None), f"Models loaded from {search_directory}."
-
-
-
-    def download_preset_model(selected_model: str) -> Tuple[str, str]:
-        """
-        Downloads the selected preset model.
-
-        Args:
-            selected_model (str): The key of the selected preset model.
-
-        Returns:
-            Tuple[str, str]: Status message and the path to the downloaded model.
-        """
-        model_info = llm_models.get(selected_model)
-        if not model_info:
-            return "Invalid model selection.", ""
-
-        try:
-            model_path = download_llm_model(
-                model_name=model_info["name"],
-                model_url=model_info["url"],
-                model_filename=model_info["filename"],
-                model_hash=model_info["hash"]
-            )
-            return f"Model '{model_info['name']}' downloaded successfully.", model_path
-        except Exception as e:
-            logging.error(f"Error downloading model: {e}")
-            return f"Failed to download model: {e}", ""
-
-    with gr.TabItem("Local LLM with Llamafile", visible=True):
-        gr.Markdown("# Settings for Llamafile")
-
-        with gr.Row():
-            with gr.Column():
-                am_noob = gr.Checkbox(label="Enable Sane Defaults", value=False, visible=True)
-                advanced_mode_toggle = gr.Checkbox(label="Advanced Mode - Show All Settings", value=False)
-                # Advanced Inputs
-                verbose_checked = gr.Checkbox(label="Enable Verbose Output", value=False, visible=False)
-                threads_checked = gr.Checkbox(label="Set CPU Threads", value=False, visible=False)
-                threads_value = gr.Number(label="Number of CPU Threads", value=None, precision=0, visible=False)
-                threads_batched_checked = gr.Checkbox(label="Enable Batched Inference", value=False, visible=False)
-                threads_batched_value = gr.Number(label="Batch Size for Inference", value=None, precision=0, visible=False)
-                model_alias_checked = gr.Checkbox(label="Set Model Alias", value=False, visible=False)
-                model_alias_value = gr.Textbox(label="Model Alias", value="", visible=False)
-                ctx_size_checked = gr.Checkbox(label="Set Prompt Context Size", value=False, visible=False)
-                ctx_size_value = gr.Number(label="Prompt Context Size", value=8124, precision=0, visible=False)
-                ngl_checked = gr.Checkbox(label="Enable GPU Layers", value=False, visible=True)
-                ngl_value = gr.Number(label="Number of GPU Layers", value=None, precision=0, visible=True)
-                batch_size_checked = gr.Checkbox(label="Set Batch Size", value=False, visible=False)
-                batch_size_value = gr.Number(label="Batch Size", value=512, visible=False)
-                memory_f32_checked = gr.Checkbox(label="Use 32-bit Floating Point", value=False, visible=False)
-                numa_checked = gr.Checkbox(label="Enable NUMA", value=False, visible=False)
-                server_timeout_value = gr.Number(label="Server Timeout", value=600, precision=0, visible=False)
-                host_checked = gr.Checkbox(label="Set IP to Listen On", value=False, visible=False)
-                host_value = gr.Textbox(label="Host IP Address", value="", visible=False)
-                port_checked = gr.Checkbox(label="Set Server Port", value=False, visible=False)
-                port_value = gr.Number(label="Port Number", value=8080, precision=0, visible=False)
-                api_key_checked = gr.Checkbox(label="Set API Key", value=False, visible=False)
-                api_key_value = gr.Textbox(label="API Key", value="", visible=False)
-                http_threads_checked = gr.Checkbox(label="Set HTTP Server Threads", value=False, visible=False)
-                http_threads_value = gr.Number(label="Number of HTTP Server Threads", value=None, precision=0, visible=False)
-                hf_repo_checked = gr.Checkbox(label="Use Huggingface Repo Model", value=False, visible=False)
-                hf_repo_value = gr.Textbox(label="Huggingface Repo Name", value="", visible=False)
-                hf_file_checked = gr.Checkbox(label="Set Huggingface Model File", value=False, visible=False)
-                hf_file_value = gr.Textbox(label="Huggingface Model File", value="", visible=False)
-
-            with gr.Column():
-                # Model Selection Section
-                gr.Markdown("## Model Selection")
-
-                # Option 1: Select from Local Filesystem
-                with gr.Row():
-                    search_directory = gr.Textbox(label="Model Directory",
-                                                  placeholder="Enter directory path(currently '.\Models')",
-                                                  value=".\Models",
-                                                  interactive=True)
-
-                # Initial population of local models
-                initial_dropdown_update, _ = update_dropdowns(".\Models")
-                refresh_button = gr.Button("Refresh Models")
-                local_model_dropdown = gr.Dropdown(label="Select Model from Directory", choices=[])
-                # Display selected model path
-                model_value = gr.Textbox(label="Selected Model File Path", value="", interactive=False)
-
-                # Option 2: Download Preset Models
-                gr.Markdown("## Download Preset Models")
-
-                preset_model_dropdown = gr.Dropdown(
-                    label="Select a Preset Model",
-                    choices=list(llm_models.keys()),
-                    value=None,
-                    interactive=True,
-                    info="Choose a preset model to download."
-                )
-                download_preset_button = gr.Button("Download Selected Preset")
-
-        with gr.Row():
-            with gr.Column():
-                start_button = gr.Button("Start Llamafile")
-                stop_button = gr.Button("Stop Llamafile (doesn't work)")
-                output_display = gr.Markdown()
-
-
-        # Show/hide advanced inputs based on toggle
-        def update_visibility(show_advanced: bool):
-            components = [
-                verbose_checked, threads_checked, threads_value,
-                http_threads_checked, http_threads_value,
-                hf_repo_checked, hf_repo_value,
-                hf_file_checked, hf_file_value,
-                ctx_size_checked, ctx_size_value,
-                ngl_checked, ngl_value,
-                host_checked, host_value,
-                port_checked, port_value
-            ]
-            return [gr.update(visible=show_advanced) for _ in components]
-
-        def on_start_button_click(
-                am_noob: bool,
-                verbose_checked: bool,
-                threads_checked: bool,
-                threads_value: Optional[int],
-                threads_batched_checked: bool,
-                threads_batched_value: Optional[int],
-                model_alias_checked: bool,
-                model_alias_value: str,
-                http_threads_checked: bool,
-                http_threads_value: Optional[int],
-                model_value: str,
-                hf_repo_checked: bool,
-                hf_repo_value: str,
-                hf_file_checked: bool,
-                hf_file_value: str,
-                ctx_size_checked: bool,
-                ctx_size_value: Optional[int],
-                ngl_checked: bool,
-                ngl_value: Optional[int],
-                batch_size_checked: bool,
-                batch_size_value: Optional[int],
-                memory_f32_checked: bool,
-                numa_checked: bool,
-                server_timeout_value: Optional[int],
-                host_checked: bool,
-                host_value: str,
-                port_checked: bool,
-                port_value: Optional[int],
-                api_key_checked: bool,
-                api_key_value: str
-        ) -> str:
-            """
-            Event handler for the Start Llamafile button.
-            """
-            try:
-                result = start_llamafile(
-                    am_noob,
-                    verbose_checked,
-                    threads_checked,
-                    threads_value,
-                    threads_batched_checked,
-                    threads_batched_value,
-                    model_alias_checked,
-                    model_alias_value,
-                    http_threads_checked,
-                    http_threads_value,
-                    model_value,
-                    hf_repo_checked,
-                    hf_repo_value,
-                    hf_file_checked,
-                    hf_file_value,
-                    ctx_size_checked,
-                    ctx_size_value,
-                    ngl_checked,
-                    ngl_value,
-                    batch_size_checked,
-                    batch_size_value,
-                    memory_f32_checked,
-                    numa_checked,
-                    server_timeout_value,
-                    host_checked,
-                    host_value,
-                    port_checked,
-                    port_value,
-                    api_key_checked,
-                    api_key_value
-                )
-                return result
-            except Exception as e:
-                logging.error(f"Error starting Llamafile: {e}")
-                return f"Failed to start Llamafile: {e}"
-
-        advanced_mode_toggle.change(
-            fn=update_visibility,
-            inputs=[advanced_mode_toggle],
-            outputs=[
-                verbose_checked, threads_checked, threads_value,
-                http_threads_checked, http_threads_value,
-                hf_repo_checked, hf_repo_value,
-                hf_file_checked, hf_file_value,
-                ctx_size_checked, ctx_size_value,
-                ngl_checked, ngl_value,
-                host_checked, host_value,
-                port_checked, port_value
-            ]
-        )
-
-        start_button.click(
-            fn=on_start_button_click,
-            inputs=[
-                am_noob,
-                verbose_checked,
-                threads_checked,
-                threads_value,
-                threads_batched_checked,
-                threads_batched_value,
-                model_alias_checked,
-                model_alias_value,
-                http_threads_checked,
-                http_threads_value,
-                model_value,
-                hf_repo_checked,
-                hf_repo_value,
-                hf_file_checked,
-                hf_file_value,
-                ctx_size_checked,
-                ctx_size_value,
-                ngl_checked,
-                ngl_value,
-                batch_size_checked,
-                batch_size_value,
-                memory_f32_checked,
-                numa_checked,
-                server_timeout_value,
-                host_checked,
-                host_value,
-                port_checked,
-                port_value,
-                api_key_checked,
-                api_key_value
-            ],
-            outputs=output_display
-        )
-
-        download_preset_button.click(
-            fn=download_preset_model,
-            inputs=[preset_model_dropdown],
-            outputs=[output_display, model_value]
-        )
-
-        # Click event for refreshing models
-        refresh_button.click(
-            fn=update_dropdowns,
-            inputs=[search_directory],  # Ensure that the directory path (string) is passed
-            outputs=[local_model_dropdown, output_display]  # Update dropdown and status
-        )
-
-        # Event to update model_value when a model is selected from the dropdown
-        local_model_dropdown.change(
-            fn=on_local_model_change,  # Function that calculates the model path
-            inputs=[local_model_dropdown, search_directory],  # Inputs: selected model and directory
-            outputs=[model_value]  # Output: Update the model_value textbox with the selected model path
-        )
-
-#
-#
-#######################################################################################################################