import gradio as gr
import os
import torch
import numpy as np
from transformers import AutoModelForImageClassification, AutoFeatureExtractor, AutoModelForCausalLM, AutoTokenizer
import time

# Dummy function to simulate model training
def simulate_training(model_type, images, audio, text, epochs, learning_rate, output_format, progress):
    for epoch in range(epochs):
        # Simulate processing
        time.sleep(1)  # Simulate time delay for training
        progress(epoch + 1, epochs)  # Update progress bar
        
        # Log training details
        log = f"Epoch {epoch + 1}/{epochs} with learning rate {learning_rate:.4f}. Processing "
        if images:
            log += f"{len(images)} images, "
        if audio:
            log += f"{len(audio)} audio files, "
        if text:
            log += f"text data: {text[:30]}..."  # Show first 30 characters of text

        if output_format == "Text":
            print(log)  # Log to console for text output
        elif output_format == "File":
            with open("training_log.txt", "a") as f:
                f.write(log + "\n")  # Log to file

    return f"Training complete! Logs saved to training_log.txt." if output_format == "File" else "Training complete!"

# Function to validate uploaded files
def validate_files(images, audio):
    valid_images = []
    valid_audio = []

    if images:
        for img in images:
            if img.endswith(('.png', '.jpg', '.jpeg')):
                valid_images.append(img)
            else:
                print(f"Invalid image file: {img}")

    if audio:
        for aud in audio:
            if aud.endswith(('.wav', '.mp3', '.flac')):
                valid_audio.append(aud)
            else:
                print(f"Invalid audio file: {aud}")

    return valid_images, valid_audio

# Function to update the chat
def update_chat(user_message, chat_history):
    bot_response = f"You said: '{user_message}'. How can I assist you further?"
    chat_history.append(("User", user_message))
    chat_history.append(("Bot", bot_response))
    return "", chat_history  # Clear input and update chat history

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# AI Model Training Interface")

    # Model type selection
    with gr.Row():
        model_type = gr.Dropdown(
            label="Select Model Type",
            choices=["Image Generation", "Audio Generation", "Text Processing"],
            value="Image Generation"
        )

    # Upload inputs
    with gr.Row():
        with gr.Column():
            images = gr.File(label="Upload Images", file_count="multiple", type="filepath")  # Corrected type
            audio = gr.File(label="Upload Audio Files", file_count="multiple", type="filepath")  # Corrected type
            text = gr.Textbox(label="Enter Text Data for Training")
        with gr.Column():
            epochs = gr.Slider(minimum=1, maximum=50, label="Number of Epochs", value=5)
            learning_rate = gr.Slider(minimum=0.0001, maximum=0.1, step=0.0001, label="Learning Rate", value=0.001)
            output_format = gr.Radio(label="Select Output Format", choices=["Text", "File"], value="Text")

    # Train button and output display
    train_button = gr.Button("Train Model")
    output = gr.Textbox(label="Output", interactive=False)

    # Progress tracking
    progress_bar = gr.Progress()  # Removed label argument

    # Chat interface
    chat_input = gr.Textbox(label="Chat with AI", placeholder="Ask your questions...")
    chat_history = gr.Chatbot(label="Chat History")  # Changed to Chatbot

    # Event handlers
    def handle_training():
        valid_images, valid_audio = validate_files(images, audio)
        if not valid_images and not valid_audio and not text:
            return "Please upload valid images, audio, or enter text data."

        output_message = simulate_training(model_type, valid_images, valid_audio, text, epochs, learning_rate, output_format, progress_bar)
        return output_message

    train_button.click(
        handle_training,
        outputs=output
    )

    chat_input.submit(update_chat, inputs=[chat_input, chat_history], outputs=[chat_input, chat_history])

# Launch the app
if __name__ == "__main__":
    demo.launch()