Deploy Gradio app with multiple files

app.py

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+import gradio as gr
+from models import analyze_content
+from utils import is_safe_image
+from config import MAX_FILE_SIZE
+
+def process_content(content):
+    if not is_safe_image(content):
+        return "Content violates safety guidelines. Please upload appropriate content."
+    
+    return analyze_content(content)
+
+with gr.Blocks(title="Visual Content Analysis") as demo:
+    gr.Markdown("## Professional Visual Content Analysis")
+    gr.Markdown("Upload images or videos for detailed analysis and feedback")
+    
+    with gr.Row():
+        with gr.Column():
+            input_media = gr.File(label="Upload Media", file_types=["image", "video"], file_count="single")
+            submit_btn = gr.Button("Analyze", variant="primary")
+        with gr.Column():
+            output = gr.Textbox(label="Analysis Report", interactive=False)
+    
+    gr.Markdown(f"*Max file size: {MAX_FILE_SIZE}MB*")
+    
+    submit_btn.click(
+        fn=process_content,
+        inputs=input_media,
+        outputs=output
+    )
+
+if __name__ == "__main__":
+    demo.launch()

config.py

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+# Application configuration
+MAX_FILE_SIZE = 10  # MB
+SAFE_CONTENT_CATEGORIES = ["professional", "educational", "artistic"]

models.py

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+from PIL import Image
+import cv2
+import numpy as np
+import random
+
+def analyze_content(file_path):
+    # Placeholder for actual analysis - in a real application, this would use computer vision models
+    # This demo provides fictional analysis for demonstration purposes
+    
+    if file_path.lower().endswith(('.mp4', '.avi', '.mov')):
+        cap = cv2.VideoCapture(file_path)
+        duration = int(cap.get(cv2.CAP_PROP_FRAME_COUNT) / cap.get(cv2.CAP_PROP_FPS)
+        cap.release()
+        media_type = "video"
+        length = f"{duration:.1f} seconds"
+    else:
+        img = Image.open(file_path)
+        width, height = img.size
+        media_type = "image"
+        length = f"{width}x{height} pixels"
+    
+    # Generate fictional analysis
+    aspects = ["composition", "lighting", "focus", "color balance", "technical quality"]
+    ratings = {aspect: random.randint(1, 10) for aspect in aspects}
+    
+    feedback = [
+        "Professional analysis report:",
+        f"Media type: {media_type}",
+        f"Dimensions/duration: {length}",
+        "\nDetailed assessment:"
+    ]
+    
+    for aspect, score in ratings.items():
+        feedback.append(f"- {aspect.capitalize()}: {score}/10")
+        if score < 4:
+            feedback.append("  (Needs significant improvement)")
+        elif score < 7:
+            feedback.append("  (Adequate but could be enhanced)")
+        else:
+            feedback.append("  (Well-executed)")
+    
+    feedback.append("\nRecommendations:")
+    feedback.append("- Consider adjusting lighting conditions")
+    feedback.append("- Ensure proper focus and framing")
+    feedback.append("- Maintain consistent color temperature")
+    
+    return "\n".join(feedback)

requirements.txt

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+gradio
+numpy
+pillow
+opencv-python
+torch
+torchvision
+scikit-image
+1. File upload capability for images and videos
+2. Content safety checks
+3. Detailed analysis report generation
+4. Professional feedback on technical aspects
+5. Size limitations for uploaded files

utils.py

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+from PIL import Image
+import numpy as np
+
+def is_safe_image(file_path):
+    """Basic content safety check (placeholder)"""
+    try:
+        img = Image.open(file_path)
+        # Convert to numpy array for analysis
+        img_array = np.array(img)
+        
+        # In a real application, this would include:
+        # 1. NSFW content detection
+        # 2. Appropriate content validation
+        # 3. Privacy compliance checks
+        
+        # For this demo, we'll just check image dimensions
+        if min(img_array.shape[:2]) < 50:
+            return False  # Reject very small images
+            
+        return True
+    except:
+        return False