app.py

import gradio as gr
import json
import subprocess
import logging
import sys
import io
import threading
import queue
from contextlib import redirect_stdout, redirect_stderr

class LiveLogHandler(logging.Handler):
    def __init__(self, log_queue):
        super().__init__()
        self.log_queue = log_queue

    def emit(self, record):
        msg = self.format(record)
        self.log_queue.put(msg)


def run_pipeline(pdf_file, system_prompt, max_step, learning_rate, epochs, model_name):
    try:
        # Construct job input
        data = {
            "input": {
                "pdf_file": pdf_file.name,
                "system_prompt": system_prompt,
                "max_step": max_step,
                "learning_rate": learning_rate,
                "epochs": epochs,
                "model_name": model_name
            }
        }
        
        # Print the start of pipeline to terminal
        print("Pipeline started with inputs:", json.dumps(data, indent=2))
        
        # Run the handler in a separate process
        process = subprocess.Popen(
            ['python3', 'handler.py', '--test_input', json.dumps(data)],
            stdout=subprocess.PIPE,
            stderr=subprocess.PIPE,
            text=True
        )
        
        # Read output in real-time and print to terminal
        stdout_lines = []
        stderr_lines = []
        
        for line in process.stdout:
            print(line.strip())  # Print stdout to terminal
            stdout_lines.append(line.strip())
        
        for line in process.stderr:
            print(f"ERROR: {line.strip()}")  # Print stderr to terminal
            stderr_lines.append(line.strip())
        
        # Wait for process to complete
        process.wait()
        
        # Try to extract JSON result
        handler_output = None
        for line in stdout_lines:
            try:
                parsed_line = json.loads(line)
                if isinstance(parsed_line, dict) and "status" in parsed_line:
                    handler_output = parsed_line
                    break
            except json.JSONDecodeError:
                continue
        
        # Prepare result
        if handler_output and handler_output.get("status") == "success":
            return {
                "model_name": handler_output.get("model_name", "N/A"),
                "processing_time": handler_output.get("processing_time", "N/A"),
                "evaluation_results": handler_output.get("evaluation_results", {})
            }
        else:
            return {
                "status": "error", 
                "details": handler_output or "No valid output received"
            }
    
    except Exception as e:
        print(f"Pipeline execution error: {str(e)}")
        return {
            "status": "error", 
            "details": str(e)
        }

# Define Gradio interface with dark theme and light blue buttons
with gr.Blocks(css='''
    .gradio-container {
        background-color: #121212;
        color: #e0e0e0;
        max-width: 800px;
        margin: 0 auto;
    }
    .custom-input {
        min-height: 50px;
        height: auto;
        background-color: #1e1e1e !important;
        color: #e0e0e0 !important;
        border: 1px solid #333 !important;
    }
    .custom-input textarea, 
    .custom-input input, 
    .custom-input .upload-icon {
        background-color: #1e1e1e !important;
        color: #e0e0e0 !important;
    }
    .gr-button {
        background-color: #87CEFA !important; /* Light Sky Blue */
        color: #121212 !important;
        border: none !important;
        font-weight: bold !important;
    }
    .gr-button:hover {
        background-color: #87CEEB !important; /* Slightly different light blue on hover */
    }
    .gr-form {
        background-color: #1e1e1e !important;
        border-color: #333 !important;
    }
    .gr-label {
        color: #87CEFA !important; /* Light Sky Blue for labels */
    }
''') as demo:
    # Add Heading at the top, centered and light blue
    title = "🤖 Fine-tuning Pipeline Configurator"
    header_style = "color: #87CEFA;"  # Light Sky Blue

    html_content = f"""
    <div style="text-align: center;">
        <h2><span style="{header_style}">{title}</span></h2>
    </div>
    """

    gr.Markdown(html_content)
    
    with gr.Column(scale=1):
        # System Prompt with consistent styling
        system_prompt = gr.Textbox(
            label="System Prompt", 
            placeholder="Enter system instructions", 
            lines=4,
            elem_classes=['custom-input'],
            value="You are a helpful assistant that provides detailed information based on the provided text."
        )

        # Numeric and model name inputs in a row with consistent styling
        with gr.Row():
            # PDF File Upload
            pdf_file = gr.File(
                label="Upload PDF", 
                file_types=[".pdf"], 
                elem_classes=['custom-input']
            )

            max_step = gr.Number(
                label="Max Steps", 
                value=150, 
                elem_classes=['custom-input']
            )
            learning_rate = gr.Number(
                label="Learning Rate", 
                value=2e-4, 
                elem_classes=['custom-input']
            )
            epochs = gr.Number(
                label="Epochs", 
                value=10, 
                elem_classes=['custom-input']
            )
            model_name = gr.Textbox(
                label="Model Name", 
                placeholder="Enter model name",
                elem_classes=['custom-input']
            )

    # Results and Run Button
    result_output = gr.JSON(label="Pipeline Results")
    run_button = gr.Button("Run Pipeline", variant="primary")

    # Trigger the function when the button is clicked
    run_button.click(
        run_pipeline,
        inputs=[pdf_file, system_prompt, max_step, learning_rate, epochs, model_name],
        outputs=[result_output]
    )

# Run Gradio app
demo.launch()