change

app.py

@@ -1,1001 +1,3 @@
-# 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()
-
-#
-# import gradio as gr
-# import subprocess
-# import os
-# import sys
-# from datetime import datetime
-# import shutil
-#
-# # 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:
-#         return "❌ ERROR: Please upload a file.", None
-#
-#     # Using .name (Corrected in previous steps)
-#     input_path = dataset_file.name
-#
-#     if not input_path.lower().endswith(".json"):
-#         return "❌ ERROR: Please upload a valid Label Studio JSON file (.json).", None
-#
-#     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
-#             # Print to console as well for debugging
-#             print(line, end='')
-#
-#         process.stdout.close()
-#         return_code = process.wait()
-#
-#         # 5. Check for successful completion
-#         if return_code == 0:
-#             log_output += "\n✅ TRAINING COMPLETE! Model saved."
-#             print("\n✅ TRAINING COMPLETE! Model saved.")
-#
-#             # 6. Verify model file exists
-#             if os.path.exists(MODEL_FILE_PATH):
-#                 file_size = os.path.getsize(MODEL_FILE_PATH) / (1024 * 1024)  # Size in MB
-#                 log_output += f"\n📦 Model file: {MODEL_FILE_PATH}"
-#                 log_output += f"\n📊 Model size: {file_size:.2f} MB"
-#                 log_output += f"\n⬇️ Click the download button below to save your model!"
-#
-#                 print(f"\n✅ Model exists at: {MODEL_FILE_PATH} ({file_size:.2f} MB)")
-#
-#                 # Create a copy in the root directory for easier access
-#                 root_copy = MODEL_FILE_NAME
-#                 try:
-#                     shutil.copy2(MODEL_FILE_PATH, root_copy)
-#                     log_output += f"\n📋 Copy created: {root_copy}"
-#                     print(f"✅ Created copy at: {root_copy}")
-#                 except Exception as e:
-#                     log_output += f"\n⚠️ Could not create root copy: {e}"
-#                     root_copy = MODEL_FILE_PATH
-#
-#                 # Return the full absolute path to ensure Gradio can find it
-#                 absolute_path = os.path.abspath(root_copy)
-#                 log_output += f"\n🔗 Download path: {absolute_path}"
-#
-#                 return log_output, absolute_path
-#             else:
-#                 log_output += f"\n⚠️ WARNING: Training completed, but model file not found at expected path ({MODEL_FILE_PATH})."
-#                 log_output += f"\n🔍 Checking directory contents..."
-#
-#                 # List files in checkpoints directory for debugging
-#                 if os.path.exists(MODEL_OUTPUT_DIR):
-#                     files = os.listdir(MODEL_OUTPUT_DIR)
-#                     log_output += f"\n📁 Files in {MODEL_OUTPUT_DIR}: {files}"
-#                 else:
-#                     log_output += f"\n❌ Directory {MODEL_OUTPUT_DIR} does not exist!"
-#
-#                 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:
-#         error_msg = f"❌ ERROR: The training script '{TRAINING_SCRIPT}' was not found. Ensure it is in the root directory of your Space."
-#         print(error_msg)
-#         return error_msg, None
-#     except Exception as e:
-#         error_msg = f"❌ An unexpected error occurred: {e}"
-#         print(error_msg)
-#         import traceback
-#         print(traceback.format_exc())
-#         return error_msg, None
-#
-#
-# # --- Gradio Interface Setup (using Blocks for a nicer layout) ---
-# with gr.Blocks(title="LayoutLMv3 Fine-Tuning App", theme=gr.themes.Soft()) 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.
-#
-#         **⚠️ IMPORTANT - Free Tier Users:**
-#         - **Download your model IMMEDIATELY** after training completes!
-#         - The model file is **temporary** and will be deleted when the Space restarts.
-#         - The download button will appear below once training is complete.
-#         - Model is saved as: **`layoutlmv3_crf_passage.pth`**
-#
-#         **⏱️ Timeout Note:** Training may timeout on free tier. Consider reducing epochs or batch size for faster training.
-#         """
-#     )
-#
-#     with gr.Row():
-#         with gr.Column(scale=1):
-#             gr.Markdown("### 📁 Dataset Upload")
-#             file_input = gr.File(
-#                 label="Upload Label Studio JSON Dataset",
-#                 file_types=[".json"]
-#             )
-#
-#             gr.Markdown("---")
-#             gr.Markdown("### ⚙️ Training Parameters")
-#
-#             batch_size_input = gr.Slider(
-#                 minimum=1, maximum=16, step=1, value=4,
-#                 label="Batch Size",
-#                 info="Smaller = less memory, slower training"
-#             )
-#             epochs_input = gr.Slider(
-#                 minimum=1, maximum=10, step=1, value=3,
-#                 label="Epochs",
-#                 info="Fewer epochs = faster training (recommended: 3-5)"
-#             )
-#             lr_input = gr.Number(
-#                 value=5e-5, label="Learning Rate",
-#                 info="Default: 5e-5"
-#             )
-#             max_len_input = gr.Slider(
-#                 minimum=128, maximum=512, step=128, value=512,
-#                 label="Max Sequence Length",
-#                 info="Shorter = faster training, less memory"
-#             )
-#
-#             train_button = gr.Button("🔥 Start Training", variant="primary", size="lg")
-#
-#         with gr.Column(scale=2):
-#             gr.Markdown("### 📊 Training Progress")
-#
-#             log_output = gr.Textbox(
-#                 label="Training Logs",
-#                 lines=25,
-#                 max_lines=30,
-#                 autoscroll=True,
-#                 show_copy_button=True,
-#                 placeholder="Click 'Start Training' to begin...\n\nLogs will appear here in real-time."
-#             )
-#
-#             gr.Markdown("### ⬇️ Download Trained Model")
-#
-#             model_download = gr.File(
-#                 label="Trained Model File (layoutlmv3_crf_passage.pth)",
-#                 interactive=False,
-#                 visible=True
-#             )
-#
-#             gr.Markdown(
-#                 """
-#                 **📥 Download Instructions:**
-#                 1. Wait for training to complete (✅ appears in logs)
-#                 2. Click the download button/icon that appears above
-#                 3. Save the `.pth` file to your local machine
-#                 4. **Do this immediately** - file is temporary!
-#
-#                 **🔧 Troubleshooting:**
-#                 - If download button doesn't appear, check the logs for errors
-#                 - Try reducing epochs or batch size if timeout occurs
-#                 - Ensure your JSON file is properly formatted
-#                 """
-#             )
-#
-#     # 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],
-#         api_name="train"
-#     )
-#
-#     # Add example info
-#     gr.Markdown(
-#         """
-#         ---
-#         ### 📖 About
-#         This Space fine-tunes LayoutLMv3 with CRF for document understanding tasks including:
-#         - Questions, Options, Answers
-#         - Section Headings
-#         - Passages
-#
-#         **Model Details:** LayoutLMv3-base + CRF layer for sequence labeling
-#         """
-#     )
-#
-# if __name__ == "__main__":
-#     demo.launch()
-
-
-# import gradio as gr
-# import subprocess
-# import os
-# import sys
-# from datetime import datetime
-# import shutil
-#
-# # 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:
-#         return "❌ ERROR: Please upload a file.", None, gr.Button(visible=False)
-#
-#     # Using .name (Corrected in previous steps)
-#     input_path = dataset_file.name
-#
-#     if not input_path.lower().endswith(".json"):
-#         return "❌ ERROR: Please upload a valid Label Studio JSON file (.json).", None, gr.Button(visible=False)
-#
-#     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
-#             # Print to console as well for debugging
-#             print(line, end='')
-#
-#         process.stdout.close()
-#         return_code = process.wait()
-#
-#         # 5. Check for successful completion
-#         if return_code == 0:
-#             log_output += "\n✅ TRAINING COMPLETE! Model saved."
-#             print("\n✅ TRAINING COMPLETE! Model saved.")
-#
-#             # 6. Verify model file exists
-#             if os.path.exists(MODEL_FILE_PATH):
-#                 file_size = os.path.getsize(MODEL_FILE_PATH) / (1024 * 1024)  # Size in MB
-#                 log_output += f"\n📦 Model file: {MODEL_FILE_PATH}"
-#                 log_output += f"\n📊 Model size: {file_size:.2f} MB"
-#
-#                 print(f"\n✅ Model exists at: {MODEL_FILE_PATH} ({file_size:.2f} MB)")
-#
-#                 # Create a copy in the root directory with timestamp for uniqueness
-#                 timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-#                 download_filename = f"layoutlmv3_trained_{timestamp}.pth"
-#
-#                 try:
-#                     shutil.copy2(MODEL_FILE_PATH, download_filename)
-#                     log_output += f"\n📋 Download file created: {download_filename}"
-#                     print(f"✅ Created download file: {download_filename}")
-#                 except Exception as e:
-#                     log_output += f"\n⚠️ Could not create download file: {e}"
-#                     download_filename = MODEL_FILE_PATH
-#
-#                 # Return the path and make download button visible
-#                 log_output += f"\n\n🎉 SUCCESS! Click the 'Download Model' button below to save your model."
-#                 log_output += f"\n⚠️ IMPORTANT: Download NOW - file will be deleted when Space restarts!"
-#
-#                 return log_output, download_filename, gr.Button(visible=True)
-#             else:
-#                 log_output += f"\n⚠️ WARNING: Training completed, but model file not found at expected path ({MODEL_FILE_PATH})."
-#                 log_output += f"\n🔍 Checking directory contents..."
-#
-#                 # List files in checkpoints directory for debugging
-#                 if os.path.exists(MODEL_OUTPUT_DIR):
-#                     files = os.listdir(MODEL_OUTPUT_DIR)
-#                     log_output += f"\n📁 Files in {MODEL_OUTPUT_DIR}: {files}"
-#                 else:
-#                     log_output += f"\n❌ Directory {MODEL_OUTPUT_DIR} does not exist!"
-#
-#                 return log_output, None, gr.Button(visible=False)
-#         else:
-#             log_output += f"\n\n❌ TRAINING FAILED with return code {return_code}. Check logs above."
-#             return log_output, None, gr.Button(visible=False)
-#
-#     except FileNotFoundError:
-#         error_msg = f"❌ ERROR: The training script '{TRAINING_SCRIPT}' was not found. Ensure it is in the root directory of your Space."
-#         print(error_msg)
-#         return error_msg, None, gr.Button(visible=False)
-#     except Exception as e:
-#         error_msg = f"❌ An unexpected error occurred: {e}"
-#         print(error_msg)
-#         import traceback
-#         print(traceback.format_exc())
-#         return error_msg, None, gr.Button(visible=False)
-#
-#
-# def download_model():
-#     """
-#     Returns the model file for download.
-#     """
-#     if os.path.exists(MODEL_FILE_PATH):
-#         return MODEL_FILE_PATH
-#     else:
-#         # Check for any .pth files in current directory
-#         pth_files = [f for f in os.listdir('.') if f.endswith('.pth')]
-#         if pth_files:
-#             return pth_files[0]
-#
-#         # Check checkpoints directory
-#         if os.path.exists(MODEL_OUTPUT_DIR):
-#             pth_files = [os.path.join(MODEL_OUTPUT_DIR, f) for f in os.listdir(MODEL_OUTPUT_DIR) if f.endswith('.pth')]
-#             if pth_files:
-#                 return pth_files[0]
-#
-#         return None
-#
-#
-# # --- Gradio Interface Setup (using Blocks for a nicer layout) ---
-# with gr.Blocks(title="LayoutLMv3 Fine-Tuning App", theme=gr.themes.Soft()) 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.
-#
-#         **⚠️ IMPORTANT - Free Tier Users:**
-#         - **Download your model IMMEDIATELY** after training completes!
-#         - The model file is **temporary** and will be deleted when the Space restarts.
-#         - A download button will appear below once training is complete.
-#
-#         **⏱️ Timeout Note:** Training may timeout on free tier. Consider reducing epochs or batch size for faster training.
-#         """
-#     )
-#
-#     with gr.Row():
-#         with gr.Column(scale=1):
-#             gr.Markdown("### 📁 Dataset Upload")
-#             file_input = gr.File(
-#                 label="Upload Label Studio JSON Dataset",
-#                 file_types=[".json"]
-#             )
-#
-#             gr.Markdown("---")
-#             gr.Markdown("### ⚙️ Training Parameters")
-#
-#             batch_size_input = gr.Slider(
-#                 minimum=1, maximum=16, step=1, value=4,
-#                 label="Batch Size",
-#                 info="Smaller = less memory, slower training"
-#             )
-#             epochs_input = gr.Slider(
-#                 minimum=1, maximum=10, step=1, value=3,
-#                 label="Epochs",
-#                 info="Fewer epochs = faster training (recommended: 3-5)"
-#             )
-#             lr_input = gr.Number(
-#                 value=5e-5, label="Learning Rate",
-#                 info="Default: 5e-5"
-#             )
-#             max_len_input = gr.Slider(
-#                 minimum=128, maximum=512, step=128, value=512,
-#                 label="Max Sequence Length",
-#                 info="Shorter = faster training, less memory"
-#             )
-#
-#             train_button = gr.Button("🔥 Start Training", variant="primary", size="lg")
-#
-#         with gr.Column(scale=2):
-#             gr.Markdown("### 📊 Training Progress")
-#
-#             log_output = gr.Textbox(
-#                 label="Training Logs",
-#                 lines=25,
-#                 max_lines=30,
-#                 autoscroll=True,
-#                 show_copy_button=True,
-#                 placeholder="Click 'Start Training' to begin...\n\nLogs will appear here in real-time."
-#             )
-#
-#             gr.Markdown("### ⬇️ Download Trained Model")
-#
-#             # Hidden state to store the file path
-#             model_path_state = gr.State(value=None)
-#
-#             # Download button (initially hidden)
-#             download_btn = gr.Button(
-#                 "📥 Download Model (.pth file)",
-#                 variant="primary",
-#                 size="lg",
-#                 visible=False
-#             )
-#
-#             # File output for download
-#             model_download = gr.File(
-#                 label="Your trained model will appear here",
-#                 interactive=False,
-#                 visible=True
-#             )
-#
-#             gr.Markdown(
-#                 """
-#                 **📥 Download Instructions:**
-#                 1. Wait for training to complete (✅ appears in logs)
-#                 2. Click the **"Download Model"** button above
-#                 3. Save the `.pth` file to your local machine
-#                 4. **Do this immediately** - file is temporary!
-#
-#                 **🔧 Troubleshooting:**
-#                 - If download button doesn't appear, check the logs for errors
-#                 - Try reducing epochs or batch size if timeout occurs
-#                 - Ensure your JSON file is properly formatted
-#                 """
-#             )
-#
-#     # Define the training action
-#     train_button.click(
-#         fn=train_model,
-#         inputs=[file_input, batch_size_input, epochs_input, lr_input, max_len_input],
-#         outputs=[log_output, model_path_state, download_btn],
-#         api_name="train"
-#     )
-#
-#     # Define the download action
-#     download_btn.click(
-#         fn=lambda path: path,
-#         inputs=[model_path_state],
-#         outputs=[model_download]
-#     )
-#
-#     # Add example info
-#     gr.Markdown(
-#         """
-#         ---
-#         ### 📖 About
-#         This Space fine-tunes LayoutLMv3 with CRF for document understanding tasks including:
-#         - Questions, Options, Answers
-#         - Section Headings
-#         - Passages
-#
-#         **Model Details:** LayoutLMv3-base + CRF layer for sequence labeling
-#         """
-#     )
-#
-# if __name__ == "__main__":
-#     demo.launch()
-
-
-
 
 import gradio as gr
 import subprocess