Update app.py

app.py

@@ -18,16 +18,88 @@ def run_inference(image):
     annotated_image = results.render()[0]
     annotated_image = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
 
-    return annotated_image
+    # Generate a summary of detected objects
+    detected_objects = results.pandas().xyxy[0]['name'].value_counts().to_dict()
+    summary = "Detected Objects:\n" + "\n".join(
+        [f"{obj}: {count}" for obj, count in detected_objects.items()]
+    )
+    return annotated_image, summary
 
 # Create the Gradio interface
-interface = gr.Interface(
-    fn=run_inference,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Image(type="pil"),
-    title="YOLOv5 Object Detection",
-    description="Upload an image to run YOLOv5 object detection and see the results."
-)
-
-# Launch the app
-interface.launch()
+interface = gr.Blocks(css="""
+body {
+    font-family: 'Poppins', sans-serif;
+    background: linear-gradient(135deg, #6a11cb, #2575fc);
+    color: #fff;
+    margin: 0;
+    padding: 0;
+}
+
+header {
+    text-align: center;
+    background: linear-gradient(90deg, #ff758c, #ff7eb3);
+    padding: 1rem;
+    border-radius: 15px;
+    box-shadow: 0px 4px 15px rgba(0, 0, 0, 0.2);
+    margin-bottom: 1rem;
+}
+
+header h1 {
+    margin: 0;
+    font-size: 2.5rem;
+    color: #fff;
+}
+
+.description {
+    text-align: center;
+    font-size: 1.2rem;
+    margin: 0.5rem 0 1.5rem 0;
+    color: #f0f0f0;
+}
+
+button {
+    background: linear-gradient(90deg, #6a11cb, #2575fc);
+    border: none;
+    border-radius: 10px;
+    padding: 0.8rem 1.5rem;
+    font-size: 1rem;
+    color: white;
+    cursor: pointer;
+    transition: transform 0.2s, background 0.2s;
+}
+
+button:hover {
+    transform: scale(1.05);
+    background: linear-gradient(90deg, #2575fc, #6a11cb);
+}
+""")
+
+with interface:
+    with gr.Row():
+        gr.Markdown("<header><h1>🌟 InsightVision: Detect, Analyze, Summarize 🌟</h1></header>")
+    with gr.Row():
+        gr.Markdown(
+            "<div class='description'>Upload an image to detect objects using YOLOv5 and receive a detailed summary of detected items!</div>"
+        )
+    with gr.Row():
+        with gr.Column():
+            uploaded_image = gr.Image(type="pil", label="Upload Your Image")
+        with gr.Column():
+            detected_image = gr.Image(type="pil", label="Detected Objects")
+            summary_output = gr.Textbox(lines=10, label="Summary of Detected Objects")
+
+    with gr.Row():
+        submit_button = gr.Button("Analyze Image")
+
+    def process_image(image):
+        annotated_image, summary = run_inference(image)
+        return annotated_image, summary
+
+    submit_button.click(
+        process_image,
+        inputs=[uploaded_image],
+        outputs=[detected_image, summary_output],
+    )
+
+if __name__ == "__main__":
+    interface.launch()