Update app.py

app.py

@@ -1,65 +1,51 @@
 import gradio as gr
 from PIL import Image, ImageEnhance, ImageOps
-import torch
-from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+import easyocr
+import re
 from datetime import datetime
 import pytz
-import re
 
-# Load model and processor
-processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-stage1")
-model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-stage1")
+# Initialize EasyOCR reader
+reader = easyocr.Reader(['en'])
 
-# Preprocess image to enhance OCR accuracy
+# Preprocessing to enhance image for OCR
 def enhance_image(image):
-    # Convert to grayscale
-    image = image.convert("L")
-    # Invert for better contrast
-    image = ImageOps.invert(image)
-    # Increase contrast and sharpness
-    image = ImageEnhance.Contrast(image).enhance(2.5)
-    image = ImageEnhance.Sharpness(image).enhance(3.0)
-    # Resize (bigger = easier to read digits clearly)
-    image = image.resize((image.width * 3, image.height * 3))
-    # Convert back to RGB for model
-    return image.convert("RGB")
-
-# Extract accurate decimal weight
+    image = image.convert("L")  # Grayscale
+    image = ImageOps.invert(image)  # Invert
+    image = ImageEnhance.Contrast(image).enhance(2.0)
+    image = ImageEnhance.Sharpness(image).enhance(2.5)
+    image = image.resize((image.width * 2, image.height * 2))  # Enlarge
+    return image
+
+# Weight detection function
 def detect_weight(image):
     try:
-        # Enhance image
         processed_image = enhance_image(image)
 
-        # OCR using Hugging Face
-        pixel_values = processor(images=processed_image, return_tensors="pt").pixel_values
-        # Use slightly longer decoding to improve accuracy
-        generated_ids = model.generate(
-            pixel_values,
-            max_length=64,
-            num_beams=4,         # Beam search to improve precision
-            early_stopping=True
-        )
-        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
+        # OCR using EasyOCR
+        result = reader.readtext(np.array(processed_image), detail=0)
+        full_text = " ".join(result)
 
-        # Extract full decimal weight like 52.75 or 18.89
-        match = re.search(r"(\d{1,4}(?:\.\d{1,4})?)", generated_text)
+        # Extract number with decimal point
+        match = re.search(r"(\d{1,4}\.\d{1,4})", full_text)
         weight = match.group(1) if match else "Not detected"
 
-        # Timestamp in IST
+        # Get IST time
         ist = pytz.timezone('Asia/Kolkata')
         current_time = datetime.now(ist).strftime("%Y-%m-%d %H:%M:%S")
 
         return f"Weight: {weight} kg\nCaptured At: {current_time} (IST)", image
+
     except Exception as e:
         return f"Error: {str(e)}", image
 
-# Gradio UI
+# Gradio interface
 interface = gr.Interface(
     fn=detect_weight,
     inputs=gr.Image(type="pil", label="Upload or Capture Image"),
     outputs=[gr.Textbox(label="Weight Info"), gr.Image(label="Snapshot")],
-    title="⚖️ Auto Weight Detector (Decimal Accurate)",
-    description="Detects full weight including decimals (e.g., 52.75 kg) from digital scale image using Hugging Face OCR."
+    title="⚖️ Accurate Auto Weight Detector",
+    description="Detects full decimal weight (e.g., 52.75 kg) using EasyOCR and shows timestamp (IST)"
 )
 
 interface.launch()