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index a6344aac8c09253b3b630fb776ae94478aa0275b..dedff2f70f5f07ab34218deb4f0059ecaffc4fe2 100644
--- a/.gitattributes
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diff --git a/README.md b/README.md
index 9ed2d23c9862c90284c17000b535cdef1d266dee..6a3e80f48fb4d5777d6dae5876af310ec8c70d45 100644
--- a/README.md
+++ b/README.md
@@ -1,15 +1,43 @@
----
-title: Warehouse Vision
-emoji: 📊
-colorFrom: blue
-colorTo: gray
-sdk: gradio
-sdk_version: 6.19.0
-python_version: '3.12'
-app_file: app.py
-pinned: false
-license: mit
-short_description: AI-powered Warehouse Object Detection
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Warehouse Vision
+emoji: 📊
+colorFrom: blue
+colorTo: gray
+sdk: gradio
+sdk_version: 6.19.0
+python_version: '3.12'
+app_file: app.py
+pinned: false
+license: mit
+short_description: AI-powered Warehouse Object Detection
+---
+# Warehouse Vision AI
+
+A YOLOv8-based warehouse object detector trained on industrial warehouse scenes.
+
+## Features
+
+- Upload your own warehouse image
+- Try 30 built-in sample images
+- View model performance metrics
+- Interactive confidence and IoU sliders
+
+## Model
+
+YOLOv8 Custom Trained
+
+## Dataset
+
+This project was trained using the SORDI Industrial Scene Dataset.
+
+Dataset source:
+https://www.kaggle.com/datasets/sordi-ai/industrial-scene-04-dataset
+
+https://www.kaggle.com/datasets/sordi-ai/industrial-scene-03-dataset
+
+The datasets are provided under the MIT License.
+
+## Deployment
+
+Hugging Face Spaces + Gradio
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..a83889d5ea0b059012046cb703b5a2da40a26ab8
--- /dev/null
+++ b/app.py
@@ -0,0 +1,216 @@
+import os
+from collections import Counter
+
+import cv2
+import gradio as gr
+import numpy as np
+from PIL import Image
+from ultralytics import YOLO
+
+# ==========================================================
+# Load Model
+# ==========================================================
+
+model = YOLO("best.pt")
+
+# ==========================================================
+# Example Images
+# ==========================================================
+
+example_images = []
+
+if os.path.exists("examples"):
+    for file in sorted(os.listdir("examples")):
+        if file.lower().endswith((".jpg", ".jpeg", ".png")):
+            example_images.append([os.path.join("examples", file)])
+
+# ==========================================================
+# Detection Function
+# ==========================================================
+
+def detect(image, conf, iou):
+
+    results = model.predict(
+        source=image,
+        conf=conf,
+        iou=iou,
+        verbose=False
+    )
+
+    result = results[0]
+
+    plotted = result.plot()
+
+    plotted = cv2.cvtColor(plotted, cv2.COLOR_BGR2RGB)
+
+    detected = []
+
+    for cls in result.boxes.cls.tolist():
+        detected.append(model.names[int(cls)])
+
+    counter = Counter(detected)
+
+    table = []
+
+    for name, count in sorted(counter.items()):
+        table.append([name, count])
+
+    return Image.fromarray(plotted), table
+
+# ==========================================================
+# Metric Images
+# ==========================================================
+
+metric_files = [
+    "metrics/results.png",
+    "metrics/P_curve.png",
+    "metrics/R_curve.png",
+    "metrics/PR_curve.png",
+    "metrics/F1_curve.png",
+    "metrics/confusion_matrix.png"
+]
+
+metric_components = []
+
+for path in metric_files:
+    if os.path.exists(path):
+        metric_components.append(gr.Image(value=path, label=os.path.basename(path)))
+
+# ==========================================================
+# About Text
+# ==========================================================
+
+about = """
+# Warehouse Vision AI
+
+### Industrial Object Detection using YOLOv8
+
+This project detects warehouse objects using a custom-trained YOLO model.
+
+### Features
+
+- Industrial Rack Detection
+- KLT Box Detection
+- Worker Detection
+- Safety Equipment Detection
+- Warehouse Object Localization
+
+### Framework
+
+- Ultralytics YOLO
+- PyTorch
+- Gradio
+- Hugging Face Spaces
+
+### Author
+
+Omkar Kalburgi
+"""
+
+# ==========================================================
+# UI
+# ==========================================================
+
+with gr.Blocks(title="Warehouse Vision AI") as demo:
+
+    gr.Markdown(
+        """
+# 📦 Warehouse Vision AI
+
+### YOLO-based Industrial Warehouse Object Detection
+
+Upload an image or try one of the sample images.
+"""
+    )
+
+    with gr.Tabs():
+
+        # --------------------------------------------------
+
+        with gr.Tab("🔍 Detection"):
+
+            with gr.Row():
+
+                with gr.Column():
+
+                    image = gr.Image(type="pil", label="Input Image")
+
+                    conf = gr.Slider(
+                        0.1,
+                        1.0,
+                        value=0.25,
+                        step=0.05,
+                        label="Confidence Threshold",
+                    )
+
+                    iou = gr.Slider(
+                        0.1,
+                        1.0,
+                        value=0.45,
+                        step=0.05,
+                        label="IoU Threshold",
+                    )
+
+                    btn = gr.Button("Run Detection")
+
+                with gr.Column():
+
+                    output = gr.Image(label="Prediction")
+
+                    table = gr.Dataframe(
+                        headers=["Class", "Count"],
+                        datatype=["str", "number"],
+                        interactive=False,
+                        label="Detected Objects",
+                    )
+
+            btn.click(
+                detect,
+                inputs=[image, conf, iou],
+                outputs=[output, table],
+            )
+
+        # --------------------------------------------------
+
+        with gr.Tab("🧪 Sample Images"):
+
+            gr.Markdown("Click any image below to test the model.")
+
+            sample_input = gr.Image(type="pil")
+
+            sample_output = gr.Image()
+
+            sample_table = gr.Dataframe(
+                headers=["Class", "Count"],
+                interactive=False,
+            )
+
+            gr.Examples(
+                examples=example_images,
+                inputs=sample_input,
+            )
+
+            sample_btn = gr.Button("Run Detection")
+
+            sample_btn.click(
+                detect,
+                inputs=[sample_input, conf, iou],
+                outputs=[sample_output, sample_table],
+            )
+
+        # --------------------------------------------------
+
+        with gr.Tab("📈 Model Performance"):
+
+            gr.Markdown("Training Metrics")
+
+            for img in metric_components:
+                img.render()
+
+        # --------------------------------------------------
+
+        with gr.Tab("📖 About"):
+
+            gr.Markdown(about)
+
+demo.launch()
\ No newline at end of file
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\ No newline at end of file