Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,41 +1,40 @@
 import streamlit as st
-import numpy as np
-import cv2
 from paddleocr import PaddleOCR
 from PIL import Image
+import numpy as np
 import re
 
+# Initialize OCR
 ocr = PaddleOCR(use_angle_cls=True, lang='en')
 
-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)
-
-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
-
-st.title("📦 Auto Weight Logger")
-uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
-camera_image = st.camera_input("Or Capture Image")
-
-input_image = uploaded_file or camera_image
-
-if input_image:
-    image = Image.open(input_image)
-    st.image(image, caption="Original Image", use_column_width=True)
-
-    processed_image = preprocess_image(image)
-    weight = extract_weight_text(processed_image)
-
-    if weight:
-        st.success(f"✅ Weight Detected: {weight} kg")
-    else:
-        st.error("❌ Weight not detected.")
+st.set_page_config(page_title="Auto Weight Logger", layout="centered")
+st.title("📷 Auto Weight Logger")
+
+# Upload image
+uploaded_file = st.file_uploader("Upload an image of the weight display", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Load image using PIL (no OpenCV)
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Convert PIL image to numpy array
+    img_array = np.array(image)
+
+    # Run OCR
+    with st.spinner("Detecting weight..."):
+        result = ocr.ocr(img_array, cls=True)
+
+        weight = "Not detected"
+        confidence = 0.0
+
+        for line in result[0]:
+            for word_info in line:
+                text, conf = word_info[1]
+                match = re.search(r'\d+\.\d+|\d+', text)
+                if match:
+                    weight = match.group()
+                    confidence = conf
+                    break
+
+        st.markdown(f"### ✅ Weight: **{weight} kg** (Confidence: {confidence:.2%})")