Update app.py

app.py

@@ -1,47 +1,3 @@
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-Update app.py
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 import gradio as gr
 import mlflow
 import os
@@ -52,34 +8,28 @@ from PIL import Image
 import cv2
 import numpy as np
 
-# ==============================
 # MLflow Configuration using Secrets
-# ==============================
 tracking_uri = os.getenv("MLFLOW_TRACKING_URI")
 username = os.getenv("MLFLOW_TRACKING_USERNAME")
 password = os.getenv("MLFLOW_TRACKING_PASSWORD")
 
 if not all([tracking_uri, username, password]):
-    raise ValueError("MLflow Secrets are not configured! Go to Space Settings → Secrets and verify the names")
+    raise ValueError("MLflow Secrets are not configured! Go to Space Settings and verify the names")
 
 os.environ["MLFLOW_TRACKING_URI"] = tracking_uri
 os.environ["MLFLOW_TRACKING_USERNAME"] = username
 os.environ["MLFLOW_TRACKING_PASSWORD"] = password
 
 mlflow.set_experiment("YOLOv12s_Inference_Logs")
-print("✅ MLflow configured successfully using secrets!")
+print("MLflow configured successfully using secrets!")
 
-# ==============================
 # Load Model
-# ==============================
 model = YOLO("Yolo12s.pt")
 
-# ==============================
 # Inference with Full MLflow Tracking
-# ==============================
 def run_inference(media_file, media_type):
     if media_file is None:
-        return None, None, None, None, "⚠️ Please upload a file first"
+        return None, None, None, None, "Please upload a file first"
     
     media_path = media_file.name
     
@@ -106,9 +56,9 @@ def run_inference(media_file, media_type):
             detections = len(results.boxes) if results.boxes is not None else 0
             mlflow.log_metric("detections_count", detections)
             
-            return img, output_img, None, None, f"✅ **Detection Complete!**\n\n🔍 **Objects Detected:** {detections}"
+            return img, output_img, None, None, f"Detection Complete! Objects Detected: {detections}"
         
-        else:  # Video
+        else:
             cap = cv2.VideoCapture(media_path)
             fps = cap.get(cv2.CAP_PROP_FPS) or 30
             w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
@@ -141,24 +91,25 @@ def run_inference(media_file, media_type):
             mlflow.log_metric("total_detections", total_detections)
             mlflow.log_metric("avg_detections_per_frame", total_detections / frame_count if frame_count > 0 else 0)
             
-            result_message = f"✅ **Video Processing Complete!**\n\n📹 **Frames Processed:** {frame_count}\n🔍 **Total Detections:** {total_detections}\n📊 **Average per Frame:** {total_detections / frame_count:.2f}"
+            avg_det = total_detections / frame_count if frame_count > 0 else 0
+            result_message = f"Video Processing Complete! Frames: {frame_count}, Total Detections: {total_detections}, Average per Frame: {avg_det:.2f}"
             
-            # Return both input and output video paths
             result_video = output_video
             
             return None, None, media_path, result_video, result_message
 
-# ==============================
-# Modern Aesthetic UI
-# ==============================
+# Modern Aesthetic UI CSS
 css = """
 @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700&display=swap');
+
 * {
     font-family: 'Inter', sans-serif;
 }
+
 body {
     background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
 }
+
 .gradio-container {
     max-width: 1400px !important;
     margin: 40px auto !important;
@@ -168,13 +119,14 @@ body {
     padding: 0 !important;
     overflow: hidden !important;
 }
-/* Header Styling */
+
 .header-container {
     background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
     padding: 48px 40px;
     text-align: center;
     border-radius: 24px 24px 0 0;
 }
+
 .header-container h1 {
     color: white !important;
     font-size: 2.8em !important;
@@ -182,16 +134,17 @@ body {
     margin: 0 0 12px 0 !important;
     text-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
 }
+
 .header-container p {
     color: rgba(255, 255, 255, 0.95) !important;
     font-size: 1.1em !important;
     margin: 0 !important;
 }
-/* Main Content Area */
+
 .main-content {
     padding: 48px 40px;
 }
-/* File Upload Area */
+
 .file-upload-area {
     background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
     border-radius: 16px;
@@ -199,12 +152,13 @@ body {
     border: 2px dashed #667eea;
     transition: all 0.3s ease;
 }
+
 .file-upload-area:hover {
     border-color: #764ba2;
     transform: translateY(-2px);
     box-shadow: 0 8px 16px rgba(102, 126, 234, 0.2);
 }
-/* Buttons */
+
 button.primary {
     background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
     border: none !important;
@@ -217,11 +171,12 @@ button.primary {
     transition: all 0.3s ease !important;
     box-shadow: 0 4px 12px rgba(102, 126, 234, 0.4) !important;
 }
+
 button.primary:hover {
     transform: translateY(-2px) !important;
     box-shadow: 0 8px 20px rgba(102, 126, 234, 0.6) !important;
 }
-/* Radio Buttons */
+
 .radio-group label {
     background: white;
     padding: 12px 24px;
@@ -230,39 +185,43 @@ button.primary:hover {
     cursor: pointer;
     transition: all 0.3s ease;
 }
+
 .radio-group label:hover {
     border-color: #667eea;
     background: #f5f7fa;
 }
-/* Output Areas */
+
 .output-image, .output-video {
     border-radius: 16px;
     overflow: hidden;
     box-shadow: 0 8px 24px rgba(0, 0, 0, 0.1);
     background: white;
 }
-/* Info Box */
+
 .info-box {
     background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
     border-radius: 12px;
     padding: 24px;
     border-left: 4px solid #667eea;
 }
-/* Custom Scrollbar */
+
 ::-webkit-scrollbar {
     width: 8px;
 }
+
 ::-webkit-scrollbar-track {
     background: #f1f1f1;
 }
+
 ::-webkit-scrollbar-thumb {
     background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
     border-radius: 4px;
 }
+
 ::-webkit-scrollbar-thumb:hover {
     background: #764ba2;
 }
-/* Animations */
+
 @keyframes fadeIn {
     from {
         opacity: 0;
@@ -273,31 +232,30 @@ button.primary:hover {
         transform: translateY(0);
     }
 }
+
 .animate-in {
     animation: fadeIn 0.6s ease-out;
 }
 """
 
 with gr.Blocks() as demo:
-    # Header
     gr.HTML("""
         <div class="header-container animate-in">
-            <h1>🚗 YOLOv12s Vehicle Detection</h1>
-            <p>Detect vehicles in Egyptian streets with state-of-the-art AI • All inferences logged to DagsHub MLflow</p>
+            <h1>YOLOv12s Vehicle Detection</h1>
+            <p>Detect vehicles in Egyptian streets with state-of-the-art AI</p>
         </div>
     """)
     
     with gr.Row(elem_classes="main-content"):
-        # Left Column - Input
         with gr.Column(scale=1):
-            gr.Markdown("### 📁 Upload Media")
+            gr.Markdown("### Upload Media")
             media = gr.File(
                 label="Drop your image or video here",
                 file_types=[".jpg", ".jpeg", ".png", ".mp4", ".avi"],
                 elem_classes="file-upload-area"
             )
             
-            gr.Markdown("### 🎯 Media Type")
+            gr.Markdown("### Media Type")
             media_type = gr.Radio(
                 ["Image", "Video"],
                 label="Select type",
@@ -305,23 +263,22 @@ with gr.Blocks() as demo:
                 elem_classes="radio-group"
             )
             
-            btn = gr.Button("🚀 Run Detection", variant="primary", size="lg", elem_classes="primary")
+            btn = gr.Button("Run Detection", variant="primary", size="lg", elem_classes="primary")
             
             gr.Markdown("""
                 ---
-                ### 📊 Features
+                ### Features
                 - Real-time vehicle detection
-                - Support for images & videos
+                - Support for images and videos
                 - Auto-logging to DagsHub
                 - Detailed metrics tracking
             """)
         
-        # Right Column - Output
         with gr.Column(scale=2):
-            gr.Markdown("### 🎬 Detection Results")
+            gr.Markdown("### Detection Results")
             
             with gr.Tabs():
-                with gr.Tab("📸 Image Results"):
+                with gr.Tab("Image Results"):
                     with gr.Row():
                         img_original = gr.Image(
                             label="Original Image",
@@ -334,7 +291,7 @@ with gr.Blocks() as demo:
                             elem_classes="output-image"
                         )
                 
-                with gr.Tab("🎥 Video Results"):
+                with gr.Tab("Video Results"):
                     with gr.Row():
                         vid_original = gr.Video(
                             label="Original Video",
@@ -347,25 +304,22 @@ with gr.Blocks() as demo:
                             elem_classes="output-video"
                         )
             
-            gr.Markdown("### 📈 Run Information")
+            gr.Markdown("### Run Information")
             info = gr.Markdown(
-                "**Ready to detect...** Upload a file and click 'Run Detection' to start!",
+                "Ready to detect... Upload a file and click Run Detection to start!",
                 elem_classes="info-box"
             )
     
-    # Event Handler
     btn.click(
         fn=run_inference,
         inputs=[media, media_type],
         outputs=[img_original, img_detected, vid_original, vid_detected, info]
     )
     
-    # Footer
     gr.HTML("""
         <div style="text-align: center; padding: 32px; color: #6b7280;">
-            <p>Powered by YOLOv12s • MLflow Tracking • DagsHub Integration</p>
+            <p>Powered by YOLOv12s - MLflow Tracking - DagsHub Integration</p>
         </div>
     """)
 
-# Fixed for Gradio 6.0: moved css and theme to launch()
-demo.launch(css=css, theme=gr.themes.Soft())
+demo.launch(css=css, theme=gr.themes.Soft())