Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,30 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import os
+from PIL import Image
+from ocr_engine import extract_weight_from_image
 
-"""
-# Welcome to Streamlit!
+Install Tesseract runtime in Hugging Face Space
+os.system("apt update && apt install -y tesseract-ocr")
 
-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).
+st.set_page_config(page_title="⚖️ Auto Weight Logger", layout="centered")
+st.title("📷 Auto Weight Logger")
+st.write("Capture the weight display using your camera or upload an image")
 
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
+Option 1: Capture image from webcam
+image_file = st.camera_input("Take a photo")
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
+Option 2: Upload image from local machine
+if image_file is None:
+image_file = st.file_uploader("Or upload an image", type=["jpg", "jpeg", "png"])
 
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
+if image_file is not None:
+image = Image.open(image_file)
+st.image(image, caption="Uploaded Image", use_column_width=True)
 
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
+python
+Copy
+Edit
+with st.spinner("🔍 Extracting weight..."):
+    result = extract_weight_from_image(image)
 
-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])),
-    ))
+st.success(f"✅ Detected Weight: {result} g")