app.py

import gradio as gr
import pandas as pd
import numpy as np
import time

# Custom CSS inspired by Google's Material Design
custom_css = """
@import url('https://fonts.googleapis.com/css2?family=Roboto:wght@300;400;500&display=swap');
body, .gradio-container {
    font-family: 'Roboto', sans-serif;
    background-color: #FAFAFA;
    color: #202124;
}
h1, h2, h3, h4, .title {
    font-weight: 400;
}
.gr-button {
    background-color: #1a73e8 !important;
    color: #fff !important;
    border-radius: 4px !important;
    font-weight: 500;
    padding: 0.6em 1.2em;
    transition: background-color 0.3s ease;
}
.gr-button:hover {
    background-color: #1669c1 !important;
}
.gr-input, .gr-textbox, .gr-file, .gr-slider input {
    border: 1px solid #dadce0 !important;
    border-radius: 4px !important;
    padding: 0.5em;
}
.gr-slider > div {
    background-color: #1a73e8 !important;
}
.tab-item {
    padding: 1em;
}
"""

# ---------------------------------
# Data Upload and Preprocessing Module
# ---------------------------------
def process_data(file, augment):
    """
    Validates and preprocesses the uploaded file.
    For CSV files: reads the CSV, shows a preview,
    and if augmentation is selected, applies simple augmentation.
    For JSONL/TXT: just displays a preview.
    """
    if file is None:
        return "No file uploaded yet."
    
    name = file.name
    ext = name.split('.')[-1].lower()
    
    if ext == "csv":
        try:
            df = pd.read_csv(file.name)
        except Exception as e:
            return f"Error reading CSV: {e}"
        
        original_preview = df.head().to_html(classes="dataframe", border=0)
        result = f"<b>Original Data (Preview):</b><br>{original_preview}"
        
        if augment:
            # Simple augmentation: add random noise to numeric columns
            df_aug = df.copy()
            num_cols = df_aug.select_dtypes(include=[np.number]).columns
            if len(num_cols) > 0:
                noise = np.random.normal(0, 0.05, df_aug[num_cols].shape)
                df_aug[num_cols] = df_aug[num_cols] + noise
                aug_preview = df_aug.head().to_html(classes="dataframe", border=0)
                result += f"<br><br><b>Augmented Data (Preview):</b><br>{aug_preview}"
            else:
                result += "<br><br><b>Note:</b> No numeric columns found for augmentation."
        return result

    elif ext == "jsonl":
        try:
            with open(file.name, "r") as f:
                lines = f.readlines()
            preview = "".join(lines[:5])
            return f"<b>File:</b> {name}<br><br><b>Preview:</b><br>{preview}"
        except Exception as e:
            return f"Error reading JSONL file: {e}"
        
    elif ext == "txt":
        try:
            with open(file.name, "r") as f:
                content = f.read(500)
            return f"<b>File:</b> {name}<br><br><b>Preview (first 500 characters):</b><br>{content}"
        except Exception as e:
            return f"Error reading TXT file: {e}"
        
    else:
        return "Unsupported file type. Please upload a CSV, JSONL, or TXT file."

data_upload_interface = gr.Interface(
    fn=process_data, 
    inputs=[
        gr.File(label="Upload CSV/JSONL/TXT File"),
        gr.Checkbox(label="Apply Data Augmentation", value=False)
    ],
    outputs=gr.HTML(),
    title="Data Upload & Preprocessing",
    description="Upload your dataset file, validate its format, and optionally apply data augmentation."
)

# ---------------------------------
# Hyperparameter Configuration Module
# ---------------------------------
def configure_hyperparameters(learning_rate, batch_size, epochs):
    config = f"<b>Learning Rate:</b> {learning_rate}<br>" + \
             f"<b>Batch Size:</b> {batch_size}<br>" + \
             f"<b>Epochs:</b> {epochs}"
    return config

hyperparameter_interface = gr.Interface(
    fn=configure_hyperparameters,
    inputs=[
        gr.Slider(0.0001, 0.1, value=0.001, label="Learning Rate", step=0.0001),
        gr.Dropdown(choices=["16", "32", "64", "128"], value="32", label="Batch Size"),
        gr.Number(value=10, label="Epochs")
    ],
    outputs=gr.HTML(),
    title="Hyperparameter Settings",
    description="Adjust the training parameters for fine-tuning the model."
)

# ---------------------------------
# Training Dashboard Module (Simulation)
# ---------------------------------
def simulate_training():
    progress_vals = []
    loss_vals = []
    for i in range(1, 101):
        time.sleep(0.03)  # Simulate training iteration delay
        progress_vals.append(i)
        loss_vals.append(np.random.rand() + (100-i)/100)  # Simulated loss curve
    
    sample_output = "This is a generated snippet from the fine-tuned Gemma model."
    return progress_vals, loss_vals, sample_output

training_interface = gr.Interface(
    fn=simulate_training,
    inputs=[],
    outputs=[
        gr.Plot(label="Training Progress (%)"),
        gr.Plot(label="Loss Curve"),
        gr.Textbox(label="Sample Output", lines=3)
    ],
    title="Training Dashboard",
    description="Monitor training progress in real-time (simulation)."
)

# ---------------------------------
# Model Export Module
# ---------------------------------
def export_model(export_format):
    time.sleep(2)  # Simulate export process
    return f"Model exported as <b>{export_format}</b>! Download link: <a href='#'>[dummy_link]</a>"

export_interface = gr.Interface(
    fn=export_model,
    inputs=gr.Radio(["TensorFlow SavedModel", "PyTorch", "GGUF"], label="Select Export Format"),
    outputs=gr.HTML(),
    title="Model Export",
    description="Export your fine-tuned model in the desired format."
)

# ---------------------------------
# Help & Documentation Module
# ---------------------------------
help_text = """
### Getting Started with Gemma Fine-tuning UI

1. **Data Upload & Preprocessing:**  
   Upload your dataset in CSV, JSONL, or TXT format. The app validates your file and shows a preview.  
   Optionally, enable data augmentation (e.g., adding random noise to numeric columns).

2. **Hyperparameter Settings:**  
   Configure training parameters such as learning rate, batch size, and epochs.

3. **Training Dashboard:**  
   Monitor the training progress in real-time. This demo simulates a training session.

4. **Model Export:**  
   Export your fine-tuned model in a variety of formats.

For more detailed documentation, please refer to the [official documentation](https://example.com).
"""

help_interface = gr.Interface(
    fn=lambda: help_text,
    inputs=[],
    outputs="markdown",
    title="Help & Documentation"
)

# ---------------------------------
# Assemble the Tabbed Interface with Gradio Blocks
# ---------------------------------
with gr.Blocks(css=custom_css) as demo:
    gr.Markdown("<h1 style='text-align: center;'>Gemma Fine-tuning UI</h1>")
    with gr.Tabs():
        with gr.TabItem("Data Upload & Preprocessing"):
            data_upload_interface.render()
        with gr.TabItem("Hyperparameters"):
            hyperparameter_interface.render()
        with gr.TabItem("Training"):
            training_interface.render()
        with gr.TabItem("Model Export"):
            export_interface.render()
        with gr.TabItem("Help"):
            help_interface.render()

demo.launch(share=True)