Update app.py

app.py

@@ -2,141 +2,104 @@ import gradio as gr
 from weight_detector import WeightDetector
 import tempfile
 import os
-from PIL import Image
-import requests
-from io import BytesIO
 
-# Initialize detector
 detector = WeightDetector()
 
 def process_input(image_source: str, image_upload=None, image_url: str = "") -> dict:
-    """Process image and return results with IST"""
+    """Process webcam/image and return weight + IST time"""
     temp_img_path = None
-    
     try:
-        # Handle different input types
-        if image_source == "upload" and image_upload is not None:
+        # Handle webcam/image upload
+        if image_source == "webcam" and image_upload is not None:
+            img = image_upload
+        elif image_source == "upload" and image_upload is not None:
             img = image_upload
         elif image_source == "url" and image_url:
+            import requests
+            from io import BytesIO
             response = requests.get(image_url)
             img = Image.open(BytesIO(response.content))
         else:
             return {
                 "weight": None,
-                "message": "No valid image provided",
+                "message": "⚠️ No image provided!",
                 "image": None,
                 "time": detector.get_current_ist()
             }
-            
-        # Save to temp file for processing
+        
+        # Save to temp file
         with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as f:
             temp_img_path = f.name
             img.save(f.name)
-            
-        # Detect weight
-        weight, time, annotated_img = detector.detect_weight(temp_img_path)
-        
-        # Format result message
-        if weight is not None:
-            message = f"✅ Detected weight: {weight:.2f}g at {time}"
-        else:
-            message = f"❌ No weight value detected at {time}"
-            
-        return {
-            "weight": weight,
-            "message": message,
-            "image": annotated_img,
-            "time": time
-        }
         
+        # Detect weight
+        return detector.detect_weight(temp_img_path)
+    
     except Exception as e:
         return {
             "weight": None,
-            "message": f"Error: {str(e)}",
+            "message": f"⚠️ Error: {str(e)}",
             "image": None,
             "time": detector.get_current_ist()
         }
     finally:
         if temp_img_path and os.path.exists(temp_img_path):
-            os.unlink(temp_img_path)
-
-# Custom CSS for better mobile display
-css = """
-#mobile-view {
-    display: none;
-}
-@media screen and (max-width: 768px) {
-    #desktop-view {
-        display: none;
-    }
-    #mobile-view {
-        display: block;
-    }
-}
-"""
+            os.remove(temp_img_path)
 
-# Gradio interface
-with gr.Blocks(title="Auto Weight Logger", css=css) as demo:
+# Gradio UI
+with gr.Blocks(title="Auto Weight Logger") as demo:
     gr.Markdown("""
-    # 🏋️ Auto Weight Logger
-    Capture or upload an image of a digital scale to automatically detect the weight value.
+    # **⚖️ Auto Weight Logger (7-Segment OCR)**
+    **Capture weight from digital balances using a webcam or image upload.**
+    - ✅ Optimized for **7-segment displays** (e.g., lab balances)
+    - 📅 Logs **IST time** automatically
+    - 🚫 Detects **blurry/glare** images
     """)
     
     with gr.Row():
         with gr.Column():
             image_source = gr.Radio(
-                ["upload", "url"],
-                label="Image Source",
-                value="upload",
-                elem_id="source-select"
+                ["webcam", "upload", "url"],
+                label="Input Source",
+                value="webcam"
             )
             
             image_upload = gr.Image(
-                sources=["upload", "webcam"],
+                sources=["webcam", "upload"],
                 type="pil",
-                label="Upload Image or Use Webcam",
-                elem_id="image-upload"
+                label="Capture/Upload Image",
+                interactive=True
             )
             
             image_url = gr.Textbox(
-                label="Image URL",
-                visible=False,
-                elem_id="image-url"
+                label="Image URL (if selected)",
+                visible=False
             )
             
             submit_btn = gr.Button("Detect Weight", variant="primary")
-            
+        
         with gr.Column():
             weight_value = gr.Number(
-                label="Detected Weight (grams)",
-                interactive=False,
-                elem_id="weight-value"
+                label="Detected Weight (g)",
+                interactive=False
             )
             
             detection_time = gr.Textbox(
                 label="Detection Time (IST)",
-                interactive=False,
-                elem_id="detection-time"
+                interactive=False
             )
             
             result_message = gr.Textbox(
                 label="Result",
-                interactive=False,
-                elem_id="result-message"
+                interactive=False
             )
             
             annotated_image = gr.Image(
                 label="Annotated Image",
-                interactive=False,
-                elem_id="annotated-image"
+                interactive=False
             )
     
-    # Mobile view toggle
-    with gr.Column(visible=False, elem_id="mobile-view"):
-        gr.Markdown("### Mobile Instructions")
-        gr.Markdown("1. Tap 'Webcam' to capture\n2. Tap 'Detect Weight'")
-    
-    # Show/hide URL input based on selection
+    # Show/hide URL input
     def toggle_url_visibility(source):
         return gr.Textbox(visible=source == "url")
     
@@ -146,17 +109,11 @@ with gr.Blocks(title="Auto Weight Logger", css=css) as demo:
         outputs=image_url
     )
     
-    # Process submission
+    # Process input
     submit_btn.click(
         process_input,
         inputs=[image_source, image_upload, image_url],
-        outputs={
-            "weight": weight_value,
-            "message": result_message,
-            "image": annotated_image,
-            "time": detection_time
-        }
+        outputs=[weight_value, detection_time, result_message, annotated_image]
     )
 
-# For Hugging Face Spaces
 demo.launch()