Update app.py with mock fridge ingredients

app.py

@@ -1,17 +1,82 @@
 import gradio as gr
-import spaces
-import torch
+import pandas as pd
+import json
 
-# 1. Keep your global imports and setups completely clean of CUDA movements.
+# Placeholder processing function mimicking an edge asset inspection pipeline
+def process_fridge_survey(input_image):
+    # This structure mirrors how an industrial site survey maps physical gear to JSON
+    mock_detected_assets = {
+        "assets": [
+            {"ingredient": "Greek Yogurt", "qty": "0.5 Liters", "status": "Fresh", "est_cost_usd": 2.50},
+            {"ingredient": "Baby Spinach", "qty": "1 Bag", "status": "Wilting (Use Immediately)", "est_cost_usd": 3.00},
+            {"ingredient": "Chicken Breast", "qty": "400 Grams", "status": "Fresh", "est_cost_usd": 6.50},
+            {"ingredient": "Yellow Onion", "qty": "1 Whole", "status": "Fresh", "est_cost_usd": 0.80}
+        ]
+    }
+    
+    # Map the unstructured visual results directly into our Digital Twin Dataframe
+    df_rows = [
+        [item['ingredient'], item['qty'], item['status'], f"${item['est_cost_usd']:.2f}"] 
+        for item in mock_detected_assets['assets']
+    ]
+    return df_rows
+
+# Build out the Operational Control Center Dashboard Layout
+with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
+    gr.Markdown("# 🛰️ Parallel Plate: Kitchen Operations & Visual Logistics Engine")
+    gr.Markdown("### *Physical-to-Digital Twin Asset Management Pipeline (Sub-32B Model Framework)*")
+    
+    with gr.Row():
+        # Left Column: Optical Ingestion Layer (The "Drone Site Survey" equivalent)
+        with gr.Column(scale=1):
+            gr.Markdown("### 📸 1. Physical Edge Ingestion")
+            image_input = gr.Image(label="Upload Fridge Optical Survey Scan", type="pil")
+            
+            with gr.Row():
+                budget_slider = gr.Slider(minimum=5, maximum=100, value=25, step=5, label="Remaining Runway Budget ($ USD)")
+                days_slider = gr.Slider(minimum=1, maximum=7, value=3, step=1, label="Target Horizon (Days)")
+                
+            scan_btn = gr.Button("🚀 Initialize Asset Optimization Scan", variant="primary")
+            
+        # Right Column: The Asset Manifest State Machine (The Digital Twin Dashboard)
+        with gr.Column(scale=1):
+            gr.Markdown("### 📊 2. Active Kitchen Digital Twin")
+            inventory_df = gr.Dataframe(
+                headers=["Ingredient Asset", "Current Volume / Qty", "Telemetry Status", "Book Value (Est)"],
+                datatype=["str", "str", "str", "str"],
+                label="Live Operational Asset Manifest",
+                interactive=True
+            )
+            
+            gr.Markdown("### 🛠️ 3. Resource Routing & Recipe Output")
+            output_text = gr.Markdown("*Awaiting asset telemetry mapping to generate optimal cooking execution paths...*")
+
+    # Define the trigger mapping link
+    scan_btn.click(
+        fn=process_fridge_survey,
+        inputs=[image_input],
+        outputs=[inventory_df]
+    )
+
+# Launch the engine
+if __name__ == "__main__":
+    demo.launch()
+
+## trial 1
+# import gradio as gr
+# import spaces
+# import torch
+
+# # 1. Keep your global imports and setups completely clean of CUDA movements.
 
-@spaces.GPU
-def greet(n):
-    # 2. Allocate tensors or load small weights directly inside the function.
-    # When this function runs, a physical GPU is fully mounted.
-    zero = torch.Tensor([0]).cuda()
-    print(f"Active Device: {zero.device}") # Will safely output 'cuda:0'
+# @spaces.GPU
+# def greet(n):
+#     # 2. Allocate tensors or load small weights directly inside the function.
+#     # When this function runs, a physical GPU is fully mounted.
+#     zero = torch.Tensor([0]).cuda()
+#     print(f"Active Device: {zero.device}") # Will safely output 'cuda:0'
     
-    return f"Hello {zero + n} Tensor"
+#     return f"Hello {zero + n} Tensor"
 
-demo = gr.Interface(fn=greet, inputs=gr.Number(), outputs=gr.Text())
-demo.launch()
+# demo = gr.Interface(fn=greet, inputs=gr.Number(), outputs=gr.Text())
+# demo.launch()