Update app.py

app.py

@@ -377,6 +377,37 @@ def run_pipeline(
             f"⚠️ Error: {exc}",
         )
 
+def run_detection_only(image, user_id):
+
+    rgb_image = ensure_numpy_image(image)
+    upload_path = write_temp_image(rgb_image)
+
+    temp_dir = Path(tempfile.mkdtemp(prefix="fridge_outputs_"))
+    output_json = temp_dir / "recipe_input.json"
+    output_image = temp_dir / "annotated_image.jpg"
+
+    detection_result = detect_and_generate(
+        image_path=upload_path,
+        credentials=ROBOFLOW_CREDENTIALS,
+        conf_threshold=0.4,
+        overlap_threshold=0.3,
+        conf_split=0.7,
+        output_json=str(output_json),
+        output_image=str(output_image),
+    )
+    Path(upload_path).unlink(missing_ok=True)
+
+    detection_payload = detection_result["recipe_json"]
+    ingredients = detection_payload.get("ingredients", [])
+
+    ingredient_rows = []
+    for ing in ingredients:
+        name = ing.get("name", "")
+        conf = ing.get("confidence", 1.0)
+        ingredient_rows.append([name, conf])
+
+    return str(output_image), detection_payload, ingredient_rows
+
 
 def record_feedback(selected_recipe: str, user_id: str, feedback_rows: List[Dict[str, Any]]):
     if not selected_recipe:
@@ -441,6 +472,23 @@ with gr.Blocks(title="Smart Fridge Recipe Assistant", theme=gr.themes.Soft()) as
                 type="pil",
                 height=350,
             )
+
+            detect_button = gr.Button("Analyze Fridge")
+            annotated_detection_preview = gr.Image(label="Annotated Detection Preview", height=300)
+            detection_payload_json_step1 = gr.JSON(label="Raw Detection Payload")
+
+            editable_ingredients = gr.Dataframe(
+                headers=["Ingredient", "Confidence"],
+                label="Detected Ingredients (editable)",
+                interactive=True,
+            )
+
+            detect_button.click(
+                fn=run_detection_only,
+                inputs=[image_input, user_id_box],
+                outputs=[annotated_detection_preview, detection_payload_json_step1, editable_ingredients],
+            )
+
             
             detection_json = gr.JSON(label="Detection payload")
             annotated_output = gr.Image(label="Annotated detection", height=350)