claudson/TRAINERGUI.py

import tkinter as tk
from tkinter import ttk, filedialog, scrolledtext
import matplotlib.pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
import threading
import subprocess
import numpy as np

class MultiModalGUI(tk.Tk):
    def __init__(self):
        super().__init__()
        self.title("Multimodal Transformer Trainer")
        self.geometry("1200x800")
        
        # Initialize model and hyperparameters
        self.model = None  # Replace with your model
        self.hparams = {"lr": 1e-4, "batch_size": 32}
        
        # Create tabs
        self.notebook = ttk.Notebook(self)
        self.training_tab = TrainingTab(self.notebook, self)
        self.inference_tab = InferenceTab(self.notebook, self)
        self.data_tab = DataTab(self.notebook, self)
        self.settings_tab = SettingsTab(self.notebook, self)
        self.notebook.add(self.training_tab, text="Training")
        self.notebook.add(self.inference_tab, text="Inference")
        self.notebook.add(self.data_tab, text="Data")
        self.notebook.add(self.settings_tab, text="Settings")
        self.notebook.pack(expand=True, fill="both")

class TrainingTab(ttk.Frame):
    def __init__(self, parent, controller):
        super().__init__(parent)
        self.controller = controller
        
        # Training progress plot
        self.figure = plt.figure(figsize=(8, 4))
        self.ax = self.figure.add_subplot(111)
        self.canvas = FigureCanvasTkAgg(self.figure, self)
        self.canvas.get_tk_widget().pack(fill=tk.BOTH, expand=True)
        
        # Start/Stop buttons
        self.start_btn = ttk.Button(self, text="Start Training", command=self.start_training)
        self.start_btn.pack(side=tk.LEFT)
        self.stop_btn = ttk.Button(self, text="Stop", command=self.stop_training)
        self.stop_btn.pack(side=tk.LEFT)
        
        # Logs
        self.logs = scrolledtext.ScrolledText(self, height=10)
        self.logs.pack(fill=tk.BOTH)
    
    def start_training(self):
        def train():
            # Example training loop (replace with actual model training)
            losses, accs = [], []
            for epoch in range(10):
                loss = np.random.rand()
                acc = np.random.rand()
                losses.append(loss)
                accs.append(acc)
                self.update_plot(losses, accs)
                self.log(f"Epoch {epoch+1}: Loss={loss:.4f}, Acc={acc:.4f}")
        threading.Thread(target=train).start()
    
    def update_plot(self, losses, accs):
        self.ax.clear()
        self.ax.plot(losses, label="Loss")
        self.ax.plot(accs, label="Accuracy")
        self.ax.legend()
        self.canvas.draw()
    
    def log(self, message):
        self.logs.insert(tk.END, message + "\n")
        self.logs.see(tk.END)
    
    def stop_training(self):
        pass  # Implement stopping logic

class InferenceTab(ttk.Frame):
    def __init__(self, parent, controller):
        super().__init__(parent)
        self.controller = controller
        
        # Task selection
        self.task_var = tk.StringVar()
        self.task_combo = ttk.Combobox(self, values=["text_generation", "image_captioning", "music_generation"])
        self.task_combo.pack()
        
        # Input/Output areas
        self.input_text = scrolledtext.ScrolledText(self, height=5)
        self.input_text.pack()
        self.output_text = scrolledtext.ScrolledText(self, height=10)
        self.output_text.pack()
        self.run_btn = ttk.Button(self, text="Run Inference", command=self.run_inference)
        self.run_btn.pack()
    
    def run_inference(self):
        task = self.task_combo.get()
        input_data = self.input_text.get("1.0", tk.END)
        # Replace with model inference
        output = f"Model output for {task}: {input_data[:20]}..."
        self.output_text.insert(tk.END, output + "\n")

class DataTab(ttk.Frame):
    def __init__(self, parent, controller):
        super().__init__(parent)
        self.controller = controller
        
        # Dataset upload
        self.upload_btn = ttk.Button(self, text="Upload Dataset", command=self.upload_dataset)
        self.upload_btn.pack()
        
        # Data preview
        self.preview_text = scrolledtext.ScrolledText(self, height=15)
        self.preview_text.pack()
    
    def upload_dataset(self):
        filetypes = [("Text", "*.txt"), ("CSV", "*.csv"), ("Images", "*.jpg")]
        filenames = filedialog.askopenfilenames(filetypes=filetypes)
        self.preview_text.insert(tk.END, f"Uploaded: {filenames}\n")

class SettingsTab(ttk.Frame):
    def __init__(self, parent, controller):
        super().__init__(parent)
        self.controller = controller
        
        # Hyperparameters
        ttk.Label(self, text="Learning Rate:").pack()
        self.lr_entry = ttk.Entry(self)
        self.lr_entry.insert(0, "0.001")
        self.lr_entry.pack()
        
        ttk.Label(self, text="Batch Size:").pack()
        self.batch_entry = ttk.Entry(self)
        self.batch_entry.insert(0, "32")
        self.batch_entry.pack()
        
        # Save/Load buttons
        self.save_btn = ttk.Button(self, text="Save Model", command=self.save_model)
        self.save_btn.pack()
        self.load_btn = ttk.Button(self, text="Load Model", command=self.load_model)
        self.load_btn.pack()
    
    def save_model(self):
        filename = filedialog.asksaveasfilename(defaultextension=".h5")
        if filename:
            self.controller.model.save_weights(filename)
    
    def load_model(self):
        filename = filedialog.askopenfilename(filetypes=[("HDF5", "*.h5")])
        if filename:
            self.controller.model.load_weights(filename)

if __name__ == "__main__":
    app = MultiModalGUI()
    app.mainloop()