import gradio as gr
import pandas as pd
from datasets import Dataset
from transformers import GPT2Tokenizer, GPT2LMHeadModel, Trainer, TrainingArguments
import torch
import os
import matplotlib.pyplot as plt

def train_model(data_file, model_name, epochs, batch_size, learning_rate):
    try:
        if not data_file.name.endswith('.csv'):
            return "Invalid file format. Please upload a CSV file.", None
        
        df = pd.read_csv(data_file.name)
        
        if 'prompt' not in df.columns or 'description' not in df.columns:
            return "CSV file must contain 'prompt' and 'description' columns.", None
        
        df['text'] = df['prompt'] + ': ' + df['description']
        dataset = Dataset.from_pandas(df[['text']])
        
        tokenizer = GPT2Tokenizer.from_pretrained(model_name)
        model = GPT2LMHeadModel.from_pretrained(model_name)
        
        if tokenizer.pad_token is None:
            tokenizer.add_special_tokens({'pad_token': '[PAD]'})
            tokenizer.pad_token = tokenizer.eos_token
        
        def tokenize_function(examples):
            tokens = tokenizer(examples['text'], padding="max_length", truncation=True, max_length=128)
            tokens['labels'] = tokens['input_ids'].copy()
            return tokens
        
        tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=['text'])
        
        training_args = TrainingArguments(
            output_dir="./results",
            overwrite_output_dir=True,
            num_train_epochs=int(epochs),
            per_device_train_batch_size=int(batch_size),
            per_device_eval_batch_size=int(batch_size),
            warmup_steps=1000,
            weight_decay=0.01,
            learning_rate=float(learning_rate),
            logging_dir="./logs",
            logging_steps=10,
            save_steps=10_000,
            save_total_limit=2,
            evaluation_strategy="steps",
            eval_steps=500,
            load_best_model_at_end=True,
            metric_for_best_model="eval_loss"
        )
        
        trainer = Trainer(
            model=model,
            args=training_args,
            train_dataset=tokenized_datasets,
            eval_dataset=tokenized_datasets,
        )
        
        trainer.train()
        eval_results = trainer.evaluate()
        
        model.save_pretrained('./fine-tuned-gpt2')
        tokenizer.save_pretrained('./fine-tuned-gpt2')
        
        train_loss = [log['loss'] for log in trainer.state.log_history if 'loss' in log]
        eval_loss = [log['eval_loss'] for log in trainer.state.log_history if 'eval_loss' in log]
        plt.plot(train_loss, label='Training Loss')
        plt.plot(eval_loss, label='Validation Loss')
        plt.xlabel('Steps')
        plt.ylabel('Loss')
        plt.title('Training and Validation Loss')
        plt.legend()
        plt.savefig('./training_eval_loss.png')
        
        return "Training completed successfully.", './training_eval_loss.png'
    except Exception as e:
        return f"An error occurred: {str(e)}", None

def generate_text(prompt, temperature, top_k, max_length, repetition_penalty, use_comma):
    try:
        model_name = "./fine-tuned-gpt2"
        tokenizer = GPT2Tokenizer.from_pretrained(model_name)
        model = GPT2LMHeadModel.from_pretrained(model_name)
        
        if use_comma:
            prompt = prompt.replace('.', ',')
        
        inputs = tokenizer(prompt, return_tensors="pt")
        input_ids = inputs.input_ids
        attention_mask = inputs.attention_mask

        outputs = model.generate(
            input_ids,
            attention_mask=attention_mask,
            max_length=int(max_length),
            temperature=float(temperature) if temperature > 0 else None,
            top_k=int(top_k) if top_k > 0 else None,
            repetition_penalty=float(repetition_penalty),
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            do_sample=True  # Assurer que do_sample est activé pour temperature et top_k
        )
        
        return tokenizer.decode(outputs[0], skip_special_tokens=True)
    except Exception as e:
        return f"An error occurred: {str(e)}"

def set_preset(preset):
    if preset == "Default":
        return 5, 8, 3e-5
    elif preset == "Fast Training":
        return 3, 16, 5e-5
    elif preset == "High Accuracy":
        return 10, 4, 1e-5

with gr.Blocks() as ui:
    gr.Markdown("# Fine-Tuner | Photographer Alpha7")
    
    with gr.Tab("Train Model"):
        with gr.Row():
            data_file = gr.File(label="Upload Data File (CSV)")
            model_name = gr.Textbox(label="Model Name", value="gpt2")
        
        with gr.Row():
            preset = gr.Radio(["Default", "Fast Training", "High Accuracy"], label="Preset")
            epochs = gr.Number(label="Epochs", value=5)
            batch_size = gr.Number(label="Batch Size", value=8)
            learning_rate = gr.Number(label="Learning Rate", value=3e-5)
        
        preset.change(set_preset, preset, [epochs, batch_size, learning_rate])
        
        train_button = gr.Button("Train Model")
        train_output = gr.Textbox(label="Training Output")
        train_graph = gr.Image(label="Training and Validation Loss Graph")
        
        train_button.click(train_model, inputs=[data_file, model_name, epochs, batch_size, learning_rate], outputs=[train_output, train_graph])
    
    with gr.Tab("Generate Text"):
        with gr.Column():
            with gr.Row():
                generated_text = gr.Textbox(label="Result view")  
            with gr.Row():
                prompt = gr.Textbox(label="Prompt")
                generate_button = gr.Button("Generate Text") 
        with gr.Column():
            temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=1.0, value=0.7)
            top_k = gr.Slider(label="Top K", minimum=1, maximum=100, value=50)
            max_length = gr.Slider(label="Max Length", minimum=10, maximum=1024, value=128)
            repetition_penalty = gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, value=1.2)
            use_comma = gr.Checkbox(label="Use Comma", value=True)
        
        generate_button.click(generate_text, inputs=[prompt, temperature, top_k, max_length, repetition_penalty, use_comma], outputs=generated_text)

ui.launch()