app.py

app.py

@@ -1,3 +1,155 @@
+# 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
@@ -25,10 +177,16 @@ def train_model(dataset_file: gr.File, batch_size: int, epochs: int, lr: float,
     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
+    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
 
-    input_path = dataset_file.path
+    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...")
 
@@ -47,6 +205,7 @@ def train_model(dataset_file: gr.File, batch_size: int, epochs: int, lr: float,
     ]
 
     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
@@ -147,4 +306,6 @@ with gr.Blocks(title="LayoutLMv3 Fine-Tuning App") as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch(server_port=7860, server_name="0.0.0.0")
+    # Removed server_port and server_name as they are often unnecessary
+    # and sometimes cause issues in managed Space environments.
+    demo.launch()