Update app.py

app.py

@@ -1,59 +1,53 @@
 import gradio as gr
-from paddleocr import PaddleOCR
-from PIL import Image
 import numpy as np
+from PIL import Image
 import cv2
 import re
+from paddleocr import PaddleOCR
 from datetime import datetime
-from pytz import timezone
-
-ocr = PaddleOCR(use_angle_cls=False, lang='en')  # cls removed
 
+# Initialize OCR model once
+ocr = PaddleOCR(use_angle_cls=True, lang='en')
+
+# Preprocessing: Convert to grayscale and threshold
+def preprocess_image(image):
+    img = np.array(image.convert("RGB"))
+    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    _, thresh = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)
+    return Image.fromarray(thresh)
+
+# OCR detection + regex filtering
+def extract_weight_text(image):
+    results = ocr.ocr(np.array(image), cls=True)
+    for line in results[0]:
+        text = line[1][0]
+        match = re.search(r"\d+\.\d+", text)
+        if match:
+            return match.group()
+    return None
+
+# Main function
 def detect_weight(image):
-    try:
-        if image is None:
-            return "No image uploaded", "N/A", None
-
-        image = image.convert("RGB")
-        image_np = np.array(image)
-
-        gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
-        enhanced = cv2.equalizeHist(gray)
-        rgb_image = cv2.cvtColor(enhanced, cv2.COLOR_GRAY2RGB)
-
-        result = ocr.ocr(rgb_image)
-
-        best_match = None
-        best_conf = 0
-
-        for line in result:
-            for box in line:
-                if len(box) > 1 and isinstance(box[1], tuple):
-                    text, conf = box[1]
-                    match = re.search(r"\d+\.\d+", text)
-                    if match and conf > best_conf:
-                        best_match = match.group()
-                        best_conf = conf
-
-        if best_match:
-            now_ist = datetime.now(timezone('Asia/Kolkata')).strftime("%Y-%m-%d %H:%M:%S IST")
-            return f"Weight: {best_match} kg (Confidence: {round(best_conf * 100, 2)}%)", now_ist, image
-        else:
-            return "No weight detected kg (Confidence: 0.0%)", "N/A", image
-
-    except Exception as e:
-        return f"Error: {str(e)}", "Error", None
-
-with gr.Blocks() as demo:
-    gr.Markdown("## Auto Weight Logger\nUpload or capture a digital scale image. This app detects the weight automatically using AI.")
-
-    with gr.Row():
-        image_input = gr.Image(type="pil", label="Upload or Capture Weight Image", sources=["upload", "camera"])
-        with gr.Column():
-            output_text = gr.Textbox(label="Detected Weight")
-            output_time = gr.Textbox(label="Captured At (IST)")
-            snapshot_output = gr.Image(label="📷 Snapshot")
-
-    image_input.change(fn=detect_weight, inputs=image_input, outputs=[output_text, output_time, snapshot_output])
-
-demo.launch()
+    if image is None:
+        return "No image uploaded.", "", None
+    pre_img = preprocess_image(image)
+    weight = extract_weight_text(pre_img)
+    if weight:
+        return f"Detected Weight: {weight} kg", datetime.now().strftime("Captured At: %Y-%m-%d %H:%M:%S"), pre_img
+    else:
+        return "Weight Not Detected", "", pre_img
+
+# Gradio UI
+interface = gr.Interface(
+    fn=detect_weight,
+    inputs=gr.Image(type="pil", label="Upload or Capture Image"),
+    outputs=[
+        gr.Textbox(label="Weight"),
+        gr.Textbox(label="Timestamp"),
+        gr.Image(label="Preprocessed Image")
+    ],
+    title="Auto Weight Logger",
+    description="Upload or click image of digital scale. It will detect and show the weight (kg).",
+)
+
+interface.launch()