Upload folder using huggingface_hub

gradio_demo.py

@@ -5,7 +5,20 @@ This app allows users to upload images or videos and see the AI analysis results
 in a user-friendly interface. It connects to the Modal-deployed Qwen3 Omni model.
 """
 
-import gradio as gr
+import sys
+import traceback
+
+print("=" * 60)
+print("🚀 Starting Media Optimization Gradio App...")
+print("=" * 60)
+
+try:
+    import gradio as gr
+    print("✅ Gradio imported successfully")
+except Exception as e:
+    print(f"❌ Failed to import Gradio: {e}")
+    sys.exit(1)
+
 import json
 import uuid
 import tempfile
@@ -13,6 +26,8 @@ import os
 from pathlib import Path
 from typing import Dict, Any, Optional
 
+print("✅ Standard libraries imported")
+
 # Import the main analysis function
 DEMO_MODE = False
 try:
@@ -301,51 +316,29 @@ def preview_media(media_file):
         return gr.update(visible=False), gr.update(visible=False)
 
 
-def preview_media(media_file):
-    """Show preview of uploaded media."""
-    if media_file is None:
-        return gr.update(visible=False), gr.update(visible=False)
-    
-    # Detect media type
-    file_ext = media_file.split(".")[-1].lower() if isinstance(media_file, str) else ""
-    is_video = file_ext in ["mp4", "mov", "avi", "mkv", "webm", "m4v"]
-    is_image = file_ext in ["jpg", "jpeg", "png", "webp", "gif", "bmp"]
-    
-    if is_image:
-        return gr.update(value=media_file, visible=True), gr.update(visible=False)
-    elif is_video:
-        return gr.update(visible=False), gr.update(value=media_file, visible=True)
-    else:
-        return gr.update(visible=False), gr.update(visible=False)
-
-
-def process_media(media_file):
+def process_media(image_file, video_file):
     """
     Main processing function that analyzes the uploaded media.
-    Returns preview updates and analysis results with persistent processing time.
+    Returns analysis results with persistent processing time.
     """
+    # Determine which file was uploaded
+    media_file = None
+    if image_file is not None:
+        media_file = image_file
+        print(f"🎬 Processing image: {media_file}")
+    elif video_file is not None:
+        media_file = video_file
+        print(f"🎬 Processing video: {media_file}")
+    
     if media_file is None:
         empty_results = format_analysis_results({"error": "Please upload a media file"})
-        return (gr.update(visible=False), gr.update(visible=False)) + empty_results
-    
-    print(f"🎬 Processing media file: {media_file}")
-    
-    # Show preview
-    file_ext = media_file.split(".")[-1].lower() if isinstance(media_file, str) else ""
-    is_video = file_ext in ["mp4", "mov", "avi", "mkv", "webm", "m4v"]
-    is_image = file_ext in ["jpg", "jpeg", "png", "webp", "gif", "bmp"]
+        return empty_results
     
     # Run analysis
     result = analyze_media(media_file)
     formatted_results = format_analysis_results(result)
     
-    # Return preview updates + results
-    if is_image:
-        return (gr.update(value=media_file, visible=True), gr.update(visible=False)) + formatted_results
-    elif is_video:
-        return (gr.update(visible=False), gr.update(value=media_file, visible=True)) + formatted_results
-    else:
-        return (gr.update(visible=False), gr.update(visible=False)) + formatted_results
+    return formatted_results
 
 
 # Custom CSS for better styling
@@ -374,8 +367,19 @@ custom_css = """
 }
 """
 
+print("=" * 60)
+print("🎨 Building Gradio Interface...")
+print("=" * 60)
+
 # Build the Gradio interface
-with gr.Blocks(css=custom_css, title="Media Optimization AI", theme=gr.themes.Soft()) as demo:
+try:
+    demo = gr.Blocks(css=custom_css, title="Media Optimization AI", theme=gr.themes.Soft())
+except Exception as e:
+    print(f"❌ Failed to create Blocks: {e}")
+    traceback.print_exc()
+    sys.exit(1)
+
+with demo:
     gr.HTML("""
         <div style="text-align: center; margin-bottom: 20px;">
             <h1>🎬 Media Optimization AI</h1>
@@ -396,27 +400,21 @@ with gr.Blocks(css=custom_css, title="Media Optimization AI", theme=gr.themes.So
     
     # Single large upload area at the top
     with gr.Group():
-        media_input = gr.File(
-            label="📎 Upload Image or Video",
-            file_types=["image", "video"],
+        gr.Markdown("### 📎 Upload Image or Video")
+        
+        # Separate upload components for images and videos
+        media_image = gr.Image(
+            label="Upload Image",
             type="filepath",
-            height=200
+            height=400,
+            sources=["upload"]
         )
         
-        # Preview area - shows uploaded media
-        with gr.Row():
-            media_preview_image = gr.Image(
-                label="Preview",
-                visible=False,
-                height=400,
-                show_label=False
-            )
-            media_preview_video = gr.Video(
-                label="Preview", 
-                visible=False,
-                height=400,
-                show_label=False
-            )
+        media_video = gr.Video(
+            label="Upload Video",
+            height=400,
+            sources=["upload"]
+        )
     
     analyze_btn = gr.Button("🚀 Analyze Media", variant="primary", size="lg", scale=1)
     
@@ -465,31 +463,27 @@ with gr.Blocks(css=custom_css, title="Media Optimization AI", theme=gr.themes.So
         with gr.Tab("📄 Raw JSON"):
             json_output = gr.Code(label="Full JSON Response", language="json")
     
-    # Show preview when file is uploaded
-    media_input.change(
-        fn=preview_media,
-        inputs=[media_input],
-        outputs=[media_preview_image, media_preview_video]
-    )
-    
-    # Connect the analyze button to the processing function
-    analyze_btn.click(
-        fn=process_media,
-        inputs=[media_input],
-        outputs=[
-            media_preview_image,      # Show image preview
-            media_preview_video,      # Show video preview
-            processing_time_output,   # Processing time (persistent)
-            summary_output,
-            viral_output,
-            engagement_output,
-            quality_output,
-            emotion_output,
-            platform_output,
-            highlights_output,
-            json_output
-        ]
-    )
+    try:
+        # Connect the analyze button to the processing function
+        analyze_btn.click(
+            fn=process_media,
+            inputs=[media_image, media_video],
+            outputs=[
+                processing_time_output,   # Processing time (persistent)
+                summary_output,
+                viral_output,
+                engagement_output,
+                quality_output,
+                emotion_output,
+                platform_output,
+                highlights_output,
+                json_output
+            ]
+        )
+        print("✅ Analyze handler registered")
+    except Exception as e:
+        print(f"❌ Failed to register event handlers: {e}")
+        traceback.print_exc()
     
     gr.Markdown("""
     ---
@@ -500,6 +494,11 @@ with gr.Blocks(css=custom_css, title="Media Optimization AI", theme=gr.themes.So
     - RAD Score represents the overall viral potential (0-100)
     """)
 
+print("=" * 60)
+print("✅ Gradio interface built successfully!")
+print(f"   Demo mode: {DEMO_MODE}")
+print("=" * 60)
+
 
 if __name__ == "__main__":
     import sys

test_gradio.py

@@ -0,0 +1,36 @@
+"""
+Quick test to verify gradio_demo.py can be imported and has valid structure
+"""
+
+import sys
+print("Testing gradio_demo.py import...")
+
+try:
+    import gradio_demo
+    print("✅ gradio_demo.py imported successfully")
+    
+    # Check if demo exists
+    if hasattr(gradio_demo, 'demo'):
+        print("✅ 'demo' object found")
+        demo = gradio_demo.demo
+        
+        # Try to get API info
+        try:
+            api_info = demo.get_api_info()
+            print(f"✅ API info retrieved: {len(api_info)} endpoints found")
+            for endpoint_name, endpoint_info in api_info.items():
+                print(f"   - {endpoint_name}")
+        except Exception as e:
+            print(f"❌ Failed to get API info: {e}")
+            import traceback
+            traceback.print_exc()
+    else:
+        print("❌ 'demo' object not found in module")
+        
+except Exception as e:
+    print(f"❌ Failed to import: {e}")
+    import traceback
+    traceback.print_exc()
+    sys.exit(1)
+
+print("\n✅ All tests passed!")