Create app.py

app.py

@@ -0,0 +1,97 @@
+import gradio as gr
+import subprocess
+import os
+import sys
+from datetime import datetime
+
+def run_training(model_name):
+    """Execute the training script and save to Hub"""
+    if not model_name.strip():
+        return "❌ Please enter a model name!"
+    
+    # Set environment variable for the script
+    os.environ['MODEL_NAME'] = model_name.strip()
+    
+    try:
+        # Run training script
+        process = subprocess.Popen(
+            [sys.executable, 'train.py'], 
+            stdout=subprocess.PIPE, 
+            stderr=subprocess.STDOUT,
+            universal_newlines=True,
+            bufsize=1
+        )
+        
+        output = ""
+        while True:
+            line = process.stdout.readline()
+            if line:
+                output += line
+                print(line.strip())  # For real-time logs
+            if process.poll() is not None:
+                break
+        
+        if process.returncode == 0:
+            output += f"\n\n✅ SUCCESS! Model saved to: https://huggingface.co/{model_name}"
+        else:
+            output += f"\n\n❌ Training failed with return code: {process.returncode}"
+            
+        return output
+        
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+# Gradio Interface
+with gr.Blocks(title="RoBERTa CUAD Trainer") as demo:
+    gr.Markdown("""
+    # 🤖 RoBERTa CUAD Question Answering Trainer
+    
+    This will train a RoBERTa model with LoRA on the CUAD dataset and save it to Hugging Face Hub.
+    
+    **Instructions:**
+    1. Enter your desired model name (format: `your-username/model-name`)
+    2. Click "Start Training"
+    3. Wait ~20-30 minutes for training to complete
+    4. Your model will be saved and publicly available!
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            model_name_input = gr.Textbox(
+                label="Model Name", 
+                placeholder="your-username/roberta-cuad-qa",
+                info="This will be your model's name on Hugging Face Hub"
+            )
+            
+            train_btn = gr.Button("🚀 Start Training", variant="primary", size="lg")
+            
+        with gr.Column():
+            gr.Markdown("""
+            **Training Details:**
+            - Model: RoBERTa-base + LoRA
+            - Dataset: CUAD (2000 samples)
+            - Time: ~20-30 minutes
+            - GPU: T4 (free)
+            """)
+    
+    output_box = gr.Textbox(
+        label="Training Output", 
+        lines=25, 
+        max_lines=50,
+        show_copy_button=True
+    )
+    
+    train_btn.click(
+        fn=run_training, 
+        inputs=model_name_input, 
+        outputs=output_box,
+        show_progress=True
+    )
+    
+    gr.Markdown("""
+    ---
+    **Note:** Make sure your model name follows the format `username/model-name` and you have write permissions.
+    """)
+
+if __name__ == "__main__":
+    demo.launch()