Update app.py

app.py

@@ -1,35 +1,66 @@
 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
 
-def run_pipeline(pdf_file, system_prompt, max_step, learning_rate, epochs, model_name):
-    # 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
-        }
-    }
+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:
-        # Call handler.py using the constructed input
-        input_json = json.dumps(data)
+        # 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', input_json],
+            ['python3', 'handler.py', '--test_input', json.dumps(data)],
             stdout=subprocess.PIPE,
             stderr=subprocess.PIPE,
             text=True
         )
-        stdout, stderr = process.communicate()
-
-        # Extract JSON object from the output
-        output_lines = stdout.splitlines()
+        
+        # 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 output_lines:
+        for line in stdout_lines:
             try:
                 parsed_line = json.loads(line)
                 if isinstance(parsed_line, dict) and "status" in parsed_line:
@@ -37,119 +68,120 @@ def run_pipeline(pdf_file, system_prompt, max_step, learning_rate, epochs, model
                     break
             except json.JSONDecodeError:
                 continue
-
-        if handler_output is None:
-            return {"status": "error", "details": f"No valid JSON found in output: {stdout}"}
-
-        # Check the status in the parsed JSON
-        if handler_output.get("status") == "success":
-            # Extract and format the result
-            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", {})
-
+        
+        # Prepare result
+        if handler_output and handler_output.get("status") == "success":
             return {
-                "model_name": model_name,
-                "processing_time": processing_time,
-                "evaluation_results": evaluation_results
+                "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 error details from the handler output
-            return handler_output
-
-    except FileNotFoundError:
-        return {"status": "error", "details": "Handler script not found"}
+            return {
+                "status": "error", 
+                "details": handler_output or "No valid output received"
+            }
+    
     except Exception as e:
-        return {"status": "error", "details": str(e)}
-
+        print(f"Pipeline execution error: {str(e)}")
+        return {
+            "status": "error", 
+            "details": str(e)
+        }
 
-# Define Gradio interface
+# Define Gradio interface with dark theme and light blue buttons
 with gr.Blocks(css='''
     .gradio-container {
-        background-color: #121212;  /* Dark background */
-        color: #f1f1f1;             /* Light text color */
-        padding: 20px;
-        font-family: 'Arial', sans-serif;
+        background-color: #121212;
+        color: #e0e0e0;
+        max-width: 800px;
+        margin: 0 auto;
     }
-
-    .gr-row {
-        margin-bottom: 20px;
+    .custom-input {
+        min-height: 50px;
+        height: auto;
+        background-color: #1e1e1e !important;
+        color: #e0e0e0 !important;
+        border: 1px solid #333 !important;
     }
-
-    /* Styling for Textboxes and Numbers */
-    input[type="text"], input[type="number"], textarea {
-        background-color: #f0f0f0;  /* Light grey background for inputs */
-        border: 1px solid #ccc;     /* Light grey border */
-        color: #000;                /* Black text inside the inputs */
-        border-radius: 8px;
-        padding: 10px;
-        font-size: 16px;
-        width: 100%;
-        box-sizing: border-box;
+    .custom-input textarea, 
+    .custom-input input, 
+    .custom-input .upload-icon {
+        background-color: #1e1e1e !important;
+        color: #e0e0e0 !important;
     }
-
-    /* Styling specific to textarea placeholder */
-    textarea::placeholder {
-        color: #999;  /* Slightly darker grey placeholder text */
-    }
-
-    /* Button styling */
-    button {
-        background-color: #4CAF50; /* Green button */
-        color: white;
-        border: none;
-        padding: 12px 20px;
-        cursor: pointer;
-        font-weight: bold;
-        font-size: 16px;
-        transition: background-color 0.3s ease;
-        border-radius: 8px;
+    .gr-button {
+        background-color: #87CEFA !important; /* Light Sky Blue */
+        color: #121212 !important;
+        border: none !important;
+        font-weight: bold !important;
     }
-
-    button:hover {
-        background-color: #3e8e41;  /* Darker green hover effect */
+    .gr-button:hover {
+        background-color: #87CEEB !important; /* Slightly different light blue on hover */
     }
-
-    /* Styling for JSON output */
-    .gr-json {
-        background-color: #333;     /* Dark background for JSON output */
-        border: 1px solid #444;     /* Slightly lighter border */
-        padding: 12px;
-        font-size: 14px;
-        max-height: 300px;
-        overflow-y: auto;
-        margin-top: 10px;
-        color: #f1f1f1;              /* Light text color */
+    .gr-form {
+        background-color: #1e1e1e !important;
+        border-color: #333 !important;
     }
-
-    /* Adjust margins for all inputs */
-    .gr-row .gr-textbox, .gr-row .gr-number {
-        margin-bottom: 15px;
+    .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."
+        )
 
-    # Add Heading at the top
-    gr.Markdown(
-        '<h2 style="color: #87CEEB; text-align: center;">🤖 Fine-tuning Pipeline Configurator</h2>'
-    )
-
-    # Layout structure with improved spacing
-    with gr.Row():
-        with gr.Column(scale=2):
-            pdf_file = gr.File(label="Upload PDF File", file_types=[".pdf"])  # Updated to accept file uploads
-        with gr.Column(scale=3):
-            system_prompt = gr.Textbox(label="System Prompt", placeholder="Enter system instructions or context", value="You are a helpful assistant that provides detailed information based on the provided text.")
-        with gr.Column(scale=2):
-            max_step = gr.Number(label="Max Steps", value=150)
-        with gr.Column(scale=2):
-            learning_rate = gr.Number(label="Learning Rate", value=2e-4)
-        with gr.Column(scale=2):
-            epochs = gr.Number(label="Epochs", value=10)
-        with gr.Column(scale=3):
-            model_name = gr.Textbox(label="Model Name", placeholder="Enter the model name")
-
+        # 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")
+    run_button = gr.Button("Run Pipeline", variant="primary")
 
     # Trigger the function when the button is clicked
     run_button.click(
@@ -159,4 +191,4 @@ with gr.Blocks(css='''
     )
 
 # Run Gradio app
-demo.launch()
+demo.launch(share=True)