Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+import matplotlib.pyplot as plt
+from transformers import pipeline
+import os
+
+# Initialize the vision agent (same as your original)
+agent = pipeline("image-classification", model="google/vit-base-patch16-224")
+
+class GradioVisionAnalyzer:
+    def __init__(self):
+        self.min_confidence = 0.1  # Default confidence threshold
+        
+    def analyze_image(self, image, confidence_threshold):
+        """Gradio-compatible analysis function"""
+        self.min_confidence = confidence_threshold/100  # Convert slider % to decimal
+        
+        try:
+            results = agent(image)
+            filtered_results = [r for r in results if r['score'] >= self.min_confidence]
+            
+            if not filtered_results:
+                return None, "No confident identifications found (adjust confidence threshold)"
+            
+            # Create visualization
+            fig = self.create_visualization(image, filtered_results)
+            return fig, self.format_results(filtered_results)
+            
+        except Exception as e:
+            return None, f"Error: {str(e)}"
+    
+    def create_visualization(self, img, results):
+        """Adapted matplotlib visualization for Gradio"""
+        plt.figure(figsize=(10, 5))
+        
+        # Show original image
+        plt.subplot(1, 2, 1)
+        plt.imshow(img)
+        plt.axis('off')
+        plt.title("Uploaded Image")
+        
+        # Show results as bar chart
+        plt.subplot(1, 2, 2)
+        labels = [r['label'] for r in results]
+        scores = [r['score'] for r in results]
+        colors = plt.cm.viridis([s/max(scores) for s in scores])
+        
+        bars = plt.barh(labels, scores, color=colors)
+        plt.xlabel('Confidence Score')
+        plt.title(f'Results (Threshold: {self.min_confidence:.0%})')
+        plt.xlim(0, 1)
+        
+        for bar in bars:
+            width = bar.get_width()
+            plt.text(min(width + 0.01, 0.99),
+                    bar.get_y() + bar.get_height()/2, 
+                    f'{width:.0%}', 
+                    va='center', 
+                    ha='left')
+        
+        plt.tight_layout()
+        return plt.gcf()  # Return the figure object
+    
+    def format_results(self, results):
+        """Format results for text output"""
+        output = f"Minimum Confidence: {self.min_confidence:.0%}\n\n"
+        for i, result in enumerate(results, 1):
+            output += f"{i}. {result['label']} ({result['score']:.0%} confidence)\n"
+        return output
+
+# Initialize analyzer
+analyzer = GradioVisionAnalyzer()
+
+# Create Gradio interface
+with gr.Blocks(title="AI Vision Agent for Security Compliance") as demo:
+    gr.Markdown("""
+    ## 🛡️ AI Security Compliance Assistant
+    Upload images to detect policy violations (unattended devices, clean-desk issues, etc.)
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="Upload Security Image")
+            confidence_slider = gr.Slider(0, 100, value=10, label="Confidence Threshold (%)")
+            analyze_btn = gr.Button("Analyze Image")
+        
+        with gr.Column():
+            plot_output = gr.Plot(label="Detection Results")
+            text_output = gr.Textbox(label="Detailed Findings", interactive=False)
+    
+    # Example images for quick testing
+    gr.Examples(
+        examples=["example1.jpg", "example2.jpg"],
+        inputs=image_input,
+        label="Try sample images"
+    )
+    
+    analyze_btn.click(
+        fn=analyzer.analyze_image,
+        inputs=[image_input, confidence_slider],
+        outputs=[plot_output, text_output]
+    )
+
+# For Hugging Face Spaces
+demo.launch()