Update app.py

app.py

@@ -1,44 +1,65 @@
 import gradio as gr
-from datetime import datetime
-from PIL import ImageFilter, ImageEnhance
-import pytz
-from ocr_engine import extract_weight_from_image
-from PIL import Image
+import easyocr
+import cv2
+import numpy as np
+import re
 
-def enhance_image(pil_img):
-    pil_img = pil_img.filter(ImageFilter.SHARPEN)
-    enhancer = ImageEnhance.Contrast(pil_img)
-    return enhancer.enhance(2.0)
+# Initialize OCR reader
+reader = easyocr.Reader(['en'], gpu=False)
 
-def process_image(img):
-    if img is None:
-        return "No image uploaded", None, None, "No OCR output"
+# Preprocessing function to enhance OCR accuracy
+def preprocess(img):
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
 
-    enhanced = enhance_image(img)
-    weight, confidence, raw_text, processed_img_cv = extract_weight_from_image(enhanced)
-    processed_img = Image.fromarray(processed_img_cv)
+    # Sharpening kernel (helps deblur weak edges)
+    sharpen_kernel = np.array([[-1, -1, -1],
+                                [-1, 9, -1],
+                                [-1, -1, -1]])
+    sharp = cv2.filter2D(gray, -1, sharpen_kernel)
 
-    ist_time = datetime.now(pytz.timezone("Asia/Kolkata")).strftime("%d-%m-%Y %I:%M:%S %p")
-    return f"{weight} kg (Confidence: {confidence}%)", ist_time, processed_img, raw_text
+    # Adaptive thresholding for binarization
+    thresh = cv2.adaptiveThreshold(sharp, 255,
+                                   cv2.ADAPTIVE_THRESH_MEAN_C,
+                                   cv2.THRESH_BINARY_INV, 11, 4)
 
-with gr.Blocks(title="⚖️ Auto Weight Logger") as demo:
-    gr.Markdown("## ⚖️ Auto Weight Logger")
-    gr.Markdown("📷 **Upload or capture an image of a digital weight scale.** This app sharpens images and extracts weights using EasyOCR.")
+    # Dilation to strengthen character boundaries
+    kernel = np.ones((2, 2), np.uint8)
+    dilated = cv2.dilate(thresh, kernel, iterations=1)
 
-    with gr.Row():
-        image_input = gr.Image(type="pil", label="Upload / Capture Image")
-        output_weight = gr.Textbox(label="⚖️ Detected Weight (in kg)")
+    return dilated
 
-    with gr.Row():
-        timestamp = gr.Textbox(label="🕒 Captured At (IST)")
-        snapshot = gr.Image(label="🧪 Processed Snapshot")
+# Extract weight using regex
+def extract_weight(results):
+    for text, _, _ in results:
+        match = re.search(r'\b\d{2,4}(\.\d{1,2})?\b', text)
+        if match:
+            return match.group()
+    return None
 
-    with gr.Row():
-        debug_output = gr.Textbox(label="🪵 Raw OCR Output")
+# Complete OCR pipeline
+def get_weight(img):
+    # Convert RGB to BGR
+    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
 
-    submit = gr.Button("🔍 Detect Weight")
-    submit.click(process_image,
-                 inputs=image_input,
-                 outputs=[output_weight, timestamp, snapshot, debug_output])
+    # Resize small or distant images (heuristic)
+    if img.shape[0] < 500:
+        img = cv2.resize(img, None, fx=2.0, fy=2.0,
+                         interpolation=cv2.INTER_CUBIC)
 
-demo.launch()
+    preprocessed = preprocess(img)
+    results = reader.readtext(preprocessed)
+    weight = extract_weight(results)
+
+    return f"Detected Weight: {weight if weight else 'Not found'}"
+
+# Gradio UI
+demo = gr.Interface(
+    fn=get_weight,
+    inputs=gr.Image(type="numpy", label="Upload Image"),
+    outputs=gr.Textbox(label="Detected Weight"),
+    title="Auto Weight Logger (Blur & Distance Aware)",
+    description="Extracts weight from digital display images using OCR & preprocessing."
+)
+
+if __name__ == "__main__":
+    demo.launch()