Update app.py

app.py

@@ -18,88 +18,90 @@ def run_inference(image):
     annotated_image = results.render()[0]
     annotated_image = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
 
-    # 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.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);
-}
-""")
+    return annotated_image
 
-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>"
-        )
+# Function to generate a summary for the detected objects
+def generate_summary(image):
+    results = model(image)
+    detected_objects = results.pandas().xyxy[0]
+    summary = "Detected objects:\n\n"
+    for idx, obj in detected_objects.iterrows():
+        summary += f"- {obj['name']} with confidence {obj['confidence']:.2f}\n"
+    return summary
+
+# Create the Gradio interface with improved UI
+with gr.Blocks(css="""
+    body {
+        font-family: 'Poppins', sans-serif;
+        background-color: #2B3D41;
+        color: #F9B9D2;
+    }
+    header {
+        background-color: #83A0A0;
+        padding: 20px;
+        text-align: center;
+        border-radius: 10px;
+        color: white;
+        box-shadow: 0 4px 10px rgba(0, 0, 0, 0.3);
+    }
+    footer {
+        background-color: #4C5F6B;
+        padding: 10px;
+        text-align: center;
+        border-radius: 10px;
+        color: white;
+        margin-top: 20px;
+        box-shadow: 0 4px 10px rgba(0, 0, 0, 0.3);
+    }
+    .btn-primary {
+        background-color: #BCA0BC;
+        color: #2B3D41;
+        padding: 10px 20px;
+        border-radius: 5px;
+        font-weight: bold;
+        border: none;
+        cursor: pointer;
+        transition: all 0.3s;
+    }
+    .btn-primary:hover {
+        background-color: #F9B9D2;
+        color: #2B3D41;
+    }
+    .gr-box {
+        background-color: #4C5F6B;
+        border-radius: 10px;
+        padding: 20px;
+        color: #F9B9D2;
+        box-shadow: 0 4px 10px rgba(0, 0, 0, 0.3);
+    }
+    .gr-input {
+        background-color: #BCA0BC;
+        border-radius: 5px;
+        border: none;
+        padding: 10px;
+        color: #2B3D41;
+    }
+""") as demo:
     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")
-
+        gr.Markdown("<h1 style='text-align:center; color:#F9B9D2;'>✨ InsightVision: Detect, Analyze, Summarize ✨</h1>")
+    
     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],
+        with gr.Column(scale=2):
+            image_input = gr.Image(label="Upload Image", type="pil", elem_classes="gr-input")
+            with gr.Row():
+                detect_button = gr.Button("Run Detection", elem_classes="btn-primary")
+        with gr.Column(scale=3):
+            annotated_image_output = gr.Image(label="Detected Image", type="pil", elem_classes="gr-box")
+            summary_output = gr.Textbox(label="Detection Summary", lines=10, interactive=False, elem_classes="gr-box")
+    
+    # Actions for buttons
+    detect_button.click(
+        fn=lambda image: (run_inference(image), generate_summary(np.array(image))),
+        inputs=[image_input],
+        outputs=[annotated_image_output, summary_output]
     )
 
-if __name__ == "__main__":
-    interface.launch()
+    gr.Markdown("<footer>Made with ❤️ using Gradio and YOLOv5 | © 2024 InsightVision</footer>")
+
+# Launch the interface
+demo.launch()