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| # import gradio as gr | |
| # import subprocess | |
| # import os | |
| # import sys | |
| # from datetime import datetime | |
| # | |
| # # The name of your existing training script | |
| # TRAINING_SCRIPT = "LayoutLM_Train_Passage.py" | |
| # | |
| # # --- CORRECTED MODEL PATH BASED ON LayoutLM_Train_Passage.py --- | |
| # MODEL_OUTPUT_DIR = "checkpoints" | |
| # MODEL_FILE_NAME = "layoutlmv3_crf_passage.pth" | |
| # MODEL_FILE_PATH = os.path.join(MODEL_OUTPUT_DIR, MODEL_FILE_NAME) | |
| # | |
| # | |
| # # ---------------------------------------------------------------- | |
| # | |
| # | |
| # def train_model(dataset_file: gr.File, batch_size: int, epochs: int, lr: float, max_len: int, progress=gr.Progress()): | |
| # """ | |
| # Handles the Gradio submission and executes the training script using subprocess. | |
| # """ | |
| # | |
| # # 1. Setup: Create output directory if it doesn't exist | |
| # os.makedirs(MODEL_OUTPUT_DIR, exist_ok=True) | |
| # | |
| # # 2. File Handling: Use the temporary path of the uploaded file | |
| # # if dataset_file is None or not dataset_file.path.endswith(".json"): | |
| # # return "❌ ERROR: Please upload a valid Label Studio JSON file.", None | |
| # | |
| # input_path = dataset_file.path | |
| # | |
| # progress(0.1, desc="Starting LayoutLMv3 Training...") | |
| # | |
| # log_output = f"--- Training Started: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} ---\n" | |
| # | |
| # # 3. Construct the subprocess command | |
| # command = [ | |
| # sys.executable, | |
| # TRAINING_SCRIPT, | |
| # "--mode", "train", | |
| # "--input", input_path, | |
| # "--batch_size", str(batch_size), | |
| # "--epochs", str(epochs), | |
| # "--lr", str(lr), | |
| # "--max_len", str(max_len) | |
| # ] | |
| # | |
| # log_output += f"Executing command: {' '.join(command)}\n\n" | |
| # | |
| # try: | |
| # # 4. Run the training script and capture output | |
| # process = subprocess.Popen( | |
| # command, | |
| # stdout=subprocess.PIPE, | |
| # stderr=subprocess.STDOUT, | |
| # text=True, | |
| # bufsize=1 | |
| # ) | |
| # | |
| # # Stream logs in real-time | |
| # for line in iter(process.stdout.readline, ""): | |
| # log_output += line | |
| # yield log_output, None # Send partial log to Gradio output | |
| # | |
| # process.stdout.close() | |
| # return_code = process.wait() | |
| # | |
| # # 5. Check for successful completion | |
| # if return_code == 0: | |
| # log_output += "\n✅ TRAINING COMPLETE! Model saved." | |
| # | |
| # # 6. Prepare download links based on script's saved path | |
| # model_exists = os.path.exists(MODEL_FILE_PATH) | |
| # | |
| # if model_exists: | |
| # log_output += f"\nModel path: {MODEL_FILE_PATH}" | |
| # # Return final log, and the file path for Gradio's download component | |
| # return log_output, MODEL_FILE_PATH | |
| # else: | |
| # log_output += f"\n⚠️ WARNING: Training completed, but model file not found at expected path ({MODEL_FILE_PATH})." | |
| # return log_output, None | |
| # else: | |
| # log_output += f"\n\n❌ TRAINING FAILED with return code {return_code}. Check logs above." | |
| # return log_output, None | |
| # | |
| # except FileNotFoundError: | |
| # return f"❌ ERROR: The training script '{TRAINING_SCRIPT}' was not found. Ensure it is in the root directory of your Space.", None | |
| # except Exception as e: | |
| # return f"❌ An unexpected error occurred: {e}", None | |
| # | |
| # | |
| # # --- Gradio Interface Setup (using Blocks for a nicer layout) --- | |
| # with gr.Blocks(title="LayoutLMv3 Fine-Tuning App") as demo: | |
| # gr.Markdown("# 🚀 LayoutLMv3 Fine-Tuning on Hugging Face Spaces") | |
| # gr.Markdown( | |
| # """ | |
| # Upload your Label Studio JSON file, set your hyperparameters, and click **Train Model** to fine-tune the LayoutLMv3 model using your script. | |
| # | |
| # **Note:** The trained model is saved in the **`checkpoints/`** folder as **`layoutlmv3_crf_passage.pth`**. | |
| # """ | |
| # ) | |
| # | |
| # with gr.Row(): | |
| # with gr.Column(scale=1): | |
| # file_input = gr.File( | |
| # label="1. Upload Label Studio JSON Dataset" | |
| # ) | |
| # | |
| # gr.Markdown("---") | |
| # gr.Markdown("### ⚙️ Training Parameters") | |
| # | |
| # batch_size_input = gr.Slider( | |
| # minimum=1, maximum=32, step=1, value=4, label="Batch Size (--batch_size)" | |
| # ) | |
| # epochs_input = gr.Slider( | |
| # minimum=1, maximum=20, step=1, value=5, label="Epochs (--epochs)" | |
| # ) | |
| # lr_input = gr.Number( | |
| # value=5e-5, label="Learning Rate (--lr)" | |
| # ) | |
| # max_len_input = gr.Number( | |
| # value=512, label="Max Sequence Length (--max_len)" | |
| # ) | |
| # | |
| # with gr.Column(scale=2): | |
| # train_button = gr.Button("🔥 Train Model", variant="primary") | |
| # | |
| # log_output = gr.Textbox( | |
| # label="Training Log Output", | |
| # lines=20, | |
| # autoscroll=True, | |
| # placeholder="Click 'Train Model' to start and see real-time logs..." | |
| # ) | |
| # | |
| # gr.Markdown("---") | |
| # gr.Markdown(f"### 🎉 Trained Model Output (Saved to `{MODEL_OUTPUT_DIR}/`)") | |
| # | |
| # # Only providing the download link for the saved .pth model file | |
| # model_download = gr.File(label=f"Trained Model File ({MODEL_FILE_NAME})", interactive=False) | |
| # | |
| # # Define the action when the button is clicked | |
| # train_button.click( | |
| # fn=train_model, | |
| # inputs=[file_input, batch_size_input, epochs_input, lr_input, max_len_input], | |
| # outputs=[log_output, model_download] | |
| # ) | |
| # | |
| # if __name__ == "__main__": | |
| # demo.launch(server_port=7860, server_name="0.0.0.0") | |
| # import gradio as gr | |
| # import subprocess | |
| # import os | |
| # import sys | |
| # from datetime import datetime | |
| # | |
| # # The name of your existing training script | |
| # TRAINING_SCRIPT = "LayoutLM_Train_Passage.py" | |
| # | |
| # # --- CORRECTED MODEL PATH BASED ON LayoutLM_Train_Passage.py --- | |
| # MODEL_OUTPUT_DIR = "checkpoints" | |
| # MODEL_FILE_NAME = "layoutlmv3_crf_passage.pth" | |
| # MODEL_FILE_PATH = os.path.join(MODEL_OUTPUT_DIR, MODEL_FILE_NAME) | |
| # | |
| # | |
| # # ---------------------------------------------------------------- | |
| # | |
| # | |
| # def train_model(dataset_file: gr.File, batch_size: int, epochs: int, lr: float, max_len: int, progress=gr.Progress()): | |
| # """ | |
| # Handles the Gradio submission and executes the training script using subprocess. | |
| # """ | |
| # | |
| # # 1. Setup: Create output directory if it doesn't exist | |
| # os.makedirs(MODEL_OUTPUT_DIR, exist_ok=True) | |
| # | |
| # # 2. File Handling: Use the temporary path of the uploaded file | |
| # if dataset_file is None: | |
| # yield "❌ ERROR: Please upload a file.", None | |
| # return | |
| # | |
| # # FIX: Gradio returns the path in the .name attribute, not .path | |
| # input_path = dataset_file.name | |
| # | |
| # if not input_path.lower().endswith(".json"): | |
| # yield "❌ ERROR: Please upload a valid Label Studio JSON file (.json).", None | |
| # return | |
| # | |
| # progress(0.1, desc="Starting LayoutLMv3 Training...") | |
| # | |
| # log_output = f"--- Training Started: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} ---\n" | |
| # | |
| # # 3. Construct the subprocess command | |
| # command = [ | |
| # sys.executable, | |
| # TRAINING_SCRIPT, | |
| # "--mode", "train", | |
| # "--input", input_path, | |
| # "--batch_size", str(batch_size), | |
| # "--epochs", str(epochs), | |
| # "--lr", str(lr), | |
| # "--max_len", str(max_len) | |
| # ] | |
| # | |
| # log_output += f"Executing command: {' '.join(command)}\n\n" | |
| # yield log_output, None # Yield the command to the log output | |
| # | |
| # try: | |
| # # 4. Run the training script and capture output | |
| # process = subprocess.Popen( | |
| # command, | |
| # stdout=subprocess.PIPE, | |
| # stderr=subprocess.STDOUT, | |
| # text=True, | |
| # bufsize=1 | |
| # ) | |
| # | |
| # # Stream logs in real-time | |
| # for line in iter(process.stdout.readline, ""): | |
| # log_output += line | |
| # yield log_output, None # Send partial log to Gradio output | |
| # | |
| # process.stdout.close() | |
| # return_code = process.wait() | |
| # | |
| # # 5. Check for successful completion | |
| # if return_code == 0: | |
| # log_output += "\n✅ TRAINING COMPLETE! Model saved." | |
| # | |
| # # 6. Prepare download links based on script's saved path | |
| # model_exists = os.path.exists(MODEL_FILE_PATH) | |
| # | |
| # if model_exists: | |
| # log_output += f"\nModel path: {MODEL_FILE_PATH}" | |
| # # Return final log, and the file path for Gradio's download component | |
| # return log_output, MODEL_FILE_PATH | |
| # else: | |
| # log_output += f"\n⚠️ WARNING: Training completed, but model file not found at expected path ({MODEL_FILE_PATH})." | |
| # return log_output, None | |
| # else: | |
| # log_output += f"\n\n❌ TRAINING FAILED with return code {return_code}. Check logs above." | |
| # return log_output, None | |
| # | |
| # except FileNotFoundError: | |
| # return f"❌ ERROR: The training script '{TRAINING_SCRIPT}' was not found. Ensure it is in the root directory of your Space.", None | |
| # except Exception as e: | |
| # return f"❌ An unexpected error occurred: {e}", None | |
| # | |
| # | |
| # # --- Gradio Interface Setup (using Blocks for a nicer layout) --- | |
| # with gr.Blocks(title="LayoutLMv3 Fine-Tuning App") as demo: | |
| # gr.Markdown("# 🚀 LayoutLMv3 Fine-Tuning on Hugging Face Spaces") | |
| # gr.Markdown( | |
| # """ | |
| # Upload your Label Studio JSON file, set your hyperparameters, and click **Train Model** to fine-tune the LayoutLMv3 model using your script. | |
| # | |
| # **Note:** The trained model is saved in the **`checkpoints/`** folder as **`layoutlmv3_crf_passage.pth`**. | |
| # """ | |
| # ) | |
| # | |
| # with gr.Row(): | |
| # with gr.Column(scale=1): | |
| # file_input = gr.File( | |
| # label="1. Upload Label Studio JSON Dataset" | |
| # ) | |
| # | |
| # gr.Markdown("---") | |
| # gr.Markdown("### ⚙️ Training Parameters") | |
| # | |
| # batch_size_input = gr.Slider( | |
| # minimum=1, maximum=32, step=1, value=4, label="Batch Size (--batch_size)" | |
| # ) | |
| # epochs_input = gr.Slider( | |
| # minimum=1, maximum=20, step=1, value=5, label="Epochs (--epochs)" | |
| # ) | |
| # lr_input = gr.Number( | |
| # value=5e-5, label="Learning Rate (--lr)" | |
| # ) | |
| # max_len_input = gr.Number( | |
| # value=512, label="Max Sequence Length (--max_len)" | |
| # ) | |
| # | |
| # with gr.Column(scale=2): | |
| # train_button = gr.Button("🔥 Train Model", variant="primary") | |
| # | |
| # log_output = gr.Textbox( | |
| # label="Training Log Output", | |
| # lines=20, | |
| # autoscroll=True, | |
| # placeholder="Click 'Train Model' to start and see real-time logs..." | |
| # ) | |
| # | |
| # gr.Markdown("---") | |
| # gr.Markdown(f"### 🎉 Trained Model Output (Saved to `{MODEL_OUTPUT_DIR}/`)") | |
| # | |
| # # Only providing the download link for the saved .pth model file | |
| # model_download = gr.File(label=f"Trained Model File ({MODEL_FILE_NAME})", interactive=False) | |
| # | |
| # # Define the action when the button is clicked | |
| # train_button.click( | |
| # fn=train_model, | |
| # inputs=[file_input, batch_size_input, epochs_input, lr_input, max_len_input], | |
| # outputs=[log_output, model_download] | |
| # ) | |
| # | |
| # if __name__ == "__main__": | |
| # # Removed server_port and server_name as they are often unnecessary | |
| # # and sometimes cause issues in managed Space environments. | |
| # demo.launch() | |
| import gradio as gr | |
| import subprocess | |
| import os | |
| import sys | |
| from datetime import datetime | |
| # FIX: Update the script name to the correct one you uploaded | |
| TRAINING_SCRIPT = "HF_LayoutLM_with_Passage.py" | |
| # --- CORRECTED MODEL PATH BASED ON YOUR SCRIPT --- | |
| MODEL_OUTPUT_DIR = "checkpoints" | |
| MODEL_FILE_NAME = "layoutlmv3_crf_passage.pth" | |
| MODEL_FILE_PATH = os.path.join(MODEL_OUTPUT_DIR, MODEL_FILE_NAME) | |
| # ---------------------------------------------------------------- | |
| def train_model(dataset_file: gr.File, batch_size: int, epochs: int, lr: float, max_len: int, progress=gr.Progress()): | |
| """ | |
| Handles the Gradio submission and executes the training script using subprocess. | |
| """ | |
| # 1. Setup: Create output directory if it doesn't exist | |
| os.makedirs(MODEL_OUTPUT_DIR, exist_ok=True) | |
| # 2. File Handling: Use the temporary path of the uploaded file | |
| if dataset_file is None: | |
| yield "❌ ERROR: Please upload a file.", None | |
| return | |
| # Using .name (Corrected in previous steps) | |
| input_path = dataset_file.name | |
| if not input_path.lower().endswith(".json"): | |
| yield "❌ ERROR: Please upload a valid Label Studio JSON file (.json).", None | |
| return | |
| progress(0.1, desc="Starting LayoutLMv3 Training...") | |
| log_output = f"--- Training Started: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} ---\n" | |
| # 3. Construct the subprocess command | |
| command = [ | |
| sys.executable, | |
| # Now uses the corrected TRAINING_SCRIPT variable | |
| TRAINING_SCRIPT, | |
| "--mode", "train", | |
| "--input", input_path, | |
| "--batch_size", str(batch_size), | |
| "--epochs", str(epochs), | |
| "--lr", str(lr), | |
| "--max_len", str(max_len) | |
| ] | |
| log_output += f"Executing command: {' '.join(command)}\n\n" | |
| yield log_output, None # Yield the command to the log output | |
| try: | |
| # 4. Run the training script and capture output | |
| process = subprocess.Popen( | |
| command, | |
| stdout=subprocess.PIPE, | |
| stderr=subprocess.STDOUT, | |
| text=True, | |
| bufsize=1 | |
| ) | |
| # Stream logs in real-time | |
| for line in iter(process.stdout.readline, ""): | |
| log_output += line | |
| yield log_output, None # Send partial log to Gradio output | |
| process.stdout.close() | |
| return_code = process.wait() | |
| # 5. Check for successful completion | |
| if return_code == 0: | |
| log_output += "\n✅ TRAINING COMPLETE! Model saved." | |
| # 6. Prepare download links based on script's saved path | |
| model_exists = os.path.exists(MODEL_FILE_PATH) | |
| if model_exists: | |
| log_output += f"\nModel path: {MODEL_FILE_PATH}" | |
| # Return final log, and the file path for Gradio's download component | |
| return log_output, MODEL_FILE_PATH | |
| else: | |
| log_output += f"\n⚠️ WARNING: Training completed, but model file not found at expected path ({MODEL_FILE_PATH})." | |
| return log_output, None | |
| else: | |
| log_output += f"\n\n❌ TRAINING FAILED with return code {return_code}. Check logs above." | |
| return log_output, None | |
| except FileNotFoundError: | |
| return f"❌ ERROR: The training script '{TRAINING_SCRIPT}' was not found. Ensure it is in the root directory of your Space.", None | |
| except Exception as e: | |
| return f"❌ An unexpected error occurred: {e}", None | |
| # --- Gradio Interface Setup (using Blocks for a nicer layout) --- | |
| with gr.Blocks(title="LayoutLMv3 Fine-Tuning App") as demo: | |
| gr.Markdown("# 🚀 LayoutLMv3 Fine-Tuning on Hugging Face Spaces") | |
| gr.Markdown( | |
| """ | |
| Upload your Label Studio JSON file, set your hyperparameters, and click **Train Model** to fine-tune the LayoutLMv3 model using your script. | |
| **Note:** The trained model is saved in the **`checkpoints/`** folder as **`layoutlmv3_crf_passage.pth`**. | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| file_input = gr.File( | |
| label="1. Upload Label Studio JSON Dataset" | |
| ) | |
| gr.Markdown("---") | |
| gr.Markdown("### ⚙️ Training Parameters") | |
| batch_size_input = gr.Slider( | |
| minimum=1, maximum=32, step=1, value=4, label="Batch Size (--batch_size)" | |
| ) | |
| epochs_input = gr.Slider( | |
| minimum=1, maximum=20, step=1, value=5, label="Epochs (--epochs)" | |
| ) | |
| lr_input = gr.Number( | |
| value=5e-5, label="Learning Rate (--lr)" | |
| ) | |
| max_len_input = gr.Number( | |
| value=512, label="Max Sequence Length (--max_len)" | |
| ) | |
| with gr.Column(scale=2): | |
| train_button = gr.Button("🔥 Train Model", variant="primary") | |
| log_output = gr.Textbox( | |
| label="Training Log Output", | |
| lines=20, | |
| autoscroll=True, | |
| placeholder="Click 'Train Model' to start and see real-time logs..." | |
| ) | |
| gr.Markdown("---") | |
| gr.Markdown(f"### 🎉 Trained Model Output (Saved to `{MODEL_OUTPUT_DIR}/`)") | |
| # Only providing the download link for the saved .pth model file | |
| model_download = gr.File(label=f"Trained Model File ({MODEL_FILE_NAME})", interactive=False) | |
| # Define the action when the button is clicked | |
| train_button.click( | |
| fn=train_model, | |
| inputs=[file_input, batch_size_input, epochs_input, lr_input, max_len_input], | |
| outputs=[log_output, model_download] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |