Rename src/streamlit_app.py to app.py

app.py

@@ -0,0 +1,56 @@
+import streamlit as st
+import numpy as np
+import cv2
+from paddleocr import PaddleOCR
+from PIL import Image
+import re
+from datetime import datetime
+import pytz
+
+# Load OCR Model
+ocr = PaddleOCR(use_angle_cls=True, lang='en')  # do not reinstall paddleocr here
+
+# Preprocess image: grayscale + 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)
+
+# Extract weight using regex
+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
+
+# Streamlit UI
+st.set_page_config(page_title="Auto Weight Logger", layout="centered")
+st.title("📦 Auto Weight Logger (Streamlit)")
+st.write("Upload or capture an image of a digital weight display to extract weight.")
+
+uploaded_img = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+camera_img = st.camera_input("Or click an image")
+
+input_img = uploaded_img or camera_img
+
+if input_img is not None:
+    image = Image.open(input_img)
+    st.image(image, caption="Original Image", use_column_width=True)
+
+    # Preprocess and show preprocessed image
+    pre_img = preprocess_image(image)
+    st.image(pre_img, caption="Preprocessed Image", use_column_width=True)
+
+    # Detect weight
+    weight = extract_weight_text(pre_img)
+
+    if weight:
+        ist_time = datetime.now(pytz.timezone("Asia/Kolkata")).strftime("%Y-%m-%d %H:%M:%S")
+        st.success(f"✅ Weight Detected: **{weight} kg**")
+        st.info(f"🕒 Captured At (IST): {ist_time}")
+    else:
+        st.error("❌ Could not detect weight. Please try with a clearer image.")

src/streamlit_app.py

@@ -1,40 +0,0 @@
-import altair as alt
-import numpy as np
-import pandas as pd
-import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))