Add application file

app.py

@@ -0,0 +1,87 @@
+import gradio as gr
+import cv2
+import numpy as np
+
+def calculate_sharpness(image):
+    """
+    Calculate the sharpness of an image using the Laplacian variance method.
+    Higher variance = sharper edges = clearer image.
+    """
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    # Calculate the variance of the Laplacian (standard measure for blurriness)
+    score = cv2.Laplacian(gray, cv2.CV_64F).var()
+    return score
+
+def process_images(files):
+    """
+    Takes a list of file paths, finds the sharpest image, and returns it along with metrics.
+    """
+    if not files:
+        raise gr.Error("Please upload at least one image.")
+
+    best_image = None
+    best_score = -1
+    best_filename = ""
+    
+    results = []
+
+    for file_path in files:
+        # Read image using OpenCV
+        # file_path is a temporary path provided by Gradio
+        img = cv2.imread(file_path)
+        
+        if img is None:
+            continue
+
+        score = calculate_sharpness(img)
+        filename = file_path.split('/')[-1] # Simple extraction of temp filename
+        
+        # Convert BGR (OpenCV standard) to RGB (Gradio standard) for display
+        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        
+        results.append({"filename": filename, "sharpness_score": round(score, 2)})
+
+        if score > best_score:
+            best_score = score
+            best_image = img_rgb
+            best_filename = filename
+
+    # Sort results for the data table
+    results.sort(key=lambda x: x["sharpness_score"], reverse=True)
+    
+    info_text = f"🏆 Best Image: **{best_filename}**\n\n**Score:** {best_score:.2f} (Laplacian Variance)"
+    
+    return best_image, info_text, results
+
+# --- Gradio UI Implementation ---
+with gr.Blocks(title="Best Image Selector") as demo:
+    gr.Markdown("# 📸 Smart Image Selector")
+    gr.Markdown("Upload multiple photos. The app will analyze them for **clarity (sharpness)** and pick the best one.")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            # Input: File uploader accepting multiple files
+            file_input = gr.File(
+                label="Upload Images", 
+                file_count="multiple", 
+                file_types=["image"]
+            )
+            process_btn = gr.Button("Find Best Image", variant="primary")
+
+        with gr.Column(scale=1):
+            # Output: Best Image display
+            output_image = gr.Image(label="The Sharpest Image", type="numpy")
+            output_info = gr.Markdown()
+            
+    # Output: Data table showing scores for all images
+    output_table = gr.JSON(label="Analysis Details")
+
+    # Wire the function
+    process_btn.click(
+        fn=process_images, 
+        inputs=file_input, 
+        outputs=[output_image, output_info, output_table]
+    )
+
+if __name__ == "__main__":
+    demo.launch()