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LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 Md Mohsin
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

app/app.py → app.py

@@ -7,7 +7,7 @@ ROOT_DIR = Path(__file__).resolve().parents[1]
 if str(ROOT_DIR) not in sys.path:
     sys.path.insert(0, str(ROOT_DIR))
 
-from inference import (
+from src.inference import (
     load_predictor,
     render_metrics,
     render_prediction_card,
@@ -159,9 +159,8 @@ def main():
 
     with st.spinner("Loading AutoCatalogAI V2 model..."):
         predictor = load_predictor(
-            repo_id=repo_id,
-            device=device,
-            top_k=selected_top_k,
+                repo_id=repo_id,
+                device=device,
         )
 
     metrics = predictor.get_model_metrics()

configs/config.yaml

@@ -26,6 +26,7 @@ data:
   color_cache_path: artifacts/cache/fashion_color_features_v2.npy
 
 model:
+  repo_id: mohsin416/autocatalogai-clip-multitask-v2
   source_repo_id: mohsin416/autocatalogai-clip-multitask
   target_repo_id: mohsin416/autocatalogai-clip-multitask-v2
   output_dir: artifacts/models/autocatalogai_v2
@@ -86,4 +87,9 @@ evaluation:
     masterCategory_accuracy: 0.93
     subCategory_accuracy: 0.88
     articleType_accuracy: 0.75
-    usage_accuracy: 0.75
+    usage_accuracy: 0.75
+
+inference:
+  device: null
+  top_k: 3
+  apply_consistency_rules: true

{app → src}/inference.py

@@ -2,31 +2,34 @@ import streamlit as st
 from autocatalog.inference.predictor import AutoCatalogPredictor
 
 @st.cache_resource(show_spinner=False)
-def load_predictor(repo_id, device, top_k):
+def load_predictor(repo_id, device=None):
     return AutoCatalogPredictor(
         repo_id=repo_id,
-        top_k=top_k
+        device=device,
     )
-    
-    
+
 def format_percent(value):
     return f"{value * 100:.2f}%"
 
-
 def render_prediction_card(task_name, task_result):
     label = task_result["label"]
     confidence = task_result["confidence"]
-    
+
     st.markdown(
         f"""
         <div class="prediction-card">
             <div class="prediction-header">
                 <span class="task-name">{task_name}</span>
-                <span class="confidence">{format_percent(confidence)}</span>
+                <span class="confidence">
+                    {format_percent(confidence)}
+                </span>
             </div>
             <div class="label">{label}</div>
             <div class="bar-bg">
-                <div class="bar-fill" style="width: {confidence * 100:.2f}%"></div>
+                <div
+                    class="bar-fill"
+                    style="width: {confidence * 100:.2f}%"
+                ></div>
             </div>
         </div>
         """,
@@ -37,25 +40,53 @@ def render_top_predictions(prediction):
     with st.expander("View Top-3 Predictions"):
         for task, result in prediction.items():
             st.markdown(f"**{task}**")
-
             for item in result["top_3"]:
-                st.write(f"{item['label']} — {format_percent(item['confidence'])}")
+                st.write(
+                    f"{item['label']} — "
+                    f"{format_percent(item['confidence'])}"
+                )
 
             st.divider()
-            
+
 
 def render_metrics(metrics):
     if not metrics:
         return
 
-    overall = metrics.get("overall_metrics", {})
+    overall = metrics.get("overall_metrics",{},)
     if not overall:
         return
 
     st.subheader("Model Evaluation")
     col1, col2, col3, col4 = st.columns(4)
-
-    col1.metric("Average Accuracy", format_percent(overall.get("average_accuracy", 0)))
-    col2.metric("Weighted F1", format_percent(overall.get("average_weighted_f1", 0)))
-    col3.metric("Top-3 Accuracy", format_percent(overall.get("average_top3_accuracy", 0)))
-    col4.metric("Test Samples", f"{overall.get('samples', 0):,}")
+    col1.metric(
+        "Average Accuracy",
+        format_percent(
+            overall.get(
+                "average_accuracy",
+                0,
+            )
+        ),
+    )
+    col2.metric(
+        "Weighted F1",
+        format_percent(
+            overall.get(
+                "average_weighted_f1",
+                0,
+            )
+        ),
+    )
+    col3.metric(
+        "Top-3 Accuracy",
+        format_percent(
+            overall.get(
+                "average_top3_accuracy",
+                0,
+            )
+        ),
+    )
+    col4.metric(
+        "Test Samples",
+        f"{overall.get('samples', 0):,}",
+    )