Update app.py

app.py

@@ -1,94 +1,29 @@
-import streamlit as st
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-import io
-import logging
+import gradio as gr
+from datetime import datetime
+import pytz
+from ocr_engine import extract_weight_from_image
 
-# Set up logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
+def process_image(img):
+    if img is None:
+        return "No image uploaded", None, None
 
-# Load the pre-trained MNIST model
-@st.cache_resource
-def load_model():
-    try:
-        model = tf.keras.models.load_model('mnist_cnn.h5')
-        logger.info("MNIST model loaded successfully")
-        return model
-    except Exception as e:
-        logger.error(f"Error loading model: {e}")
-        st.error("Failed to load the model. Please check the model file.")
-        return None
+    ist_time = datetime.now(pytz.timezone("Asia/Kolkata")).strftime("%d-%m-%Y %I:%M:%S %p")
+    weight, confidence = extract_weight_from_image(img)
+    return f"{weight} kg (Confidence: {confidence}%)", ist_time, img
 
-# Preprocess the uploaded image
-def preprocess_image(image):
-    try:
-        # Convert to grayscale
-        img = image.convert('L')
-        # Resize to 28x28 (MNIST model input size)
-        img = img.resize((28, 28), Image.Resampling.LANCZOS)
-        # Convert to numpy array and normalize
-        img_array = np.array(img)
-        # Ensure the image is inverted if necessary (MNIST expects white digits on black background)
-        img_array = 255 - img_array  # Invert colors
-        img_array = img_array / 255.0  # Normalize to [0, 1]
-        # Reshape for model input (1, 28, 28, 1)
-        img_array = img_array.reshape(1, 28, 28, 1)
-        logger.info("Image preprocessed successfully")
-        return img_array
-    except Exception as e:
-        logger.error(f"Error preprocessing image: {e}")
-        st.error("Failed to preprocess the image. Please ensure it's a valid image.")
-        return None
+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.")
 
-# Streamlit app
-st.title("AutoWeightLogger - Number Detection")
-st.write("Upload an image containing a single handwritten digit to detect the number.")
+    with gr.Row():
+        image_input = gr.Image(type="pil", label="Upload / Capture Image")
+        output_weight = gr.Textbox(label="⚖️ Detected Weight (in kg)")
 
-# File uploader
-uploaded_file = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"])
+    with gr.Row():
+        timestamp = gr.Textbox(label="🕒 Captured At (IST)")
+        snapshot = gr.Image(label="📸 Snapshot Image")
 
-if uploaded_file is not None:
-    try:
-        # Display the uploaded image
-        image = Image.open(uploaded_file)
-        st.image(image, caption="Uploaded Image", use_column_width=True)
+    submit = gr.Button("🔍 Detect Weight")
+    submit.click(process_image, inputs=image_input, outputs=[output_weight, timestamp, snapshot])
 
-        # Preprocess the image
-        processed_image = preprocess_image(image)
-        if processed_image is None:
-            st.stop()
-
-        # Load the model
-        model = load_model()
-        if model is None:
-            st.stop()
-
-        # Make prediction
-        with st.spinner("Detecting number..."):
-            prediction = model.predict(processed_image)
-            predicted_digit = np.argmax(prediction, axis=1)[0]
-            confidence = np.max(prediction) * 100
-
-        # Display result
-        st.success(f"Detected Number: {predicted_digit}")
-        st.write(f"Confidence: {confidence:.2f}%")
-
-        # Provide feedback if confidence is low
-        if confidence < 70:
-            st.warning("Low confidence in prediction. Please ensure the image contains a clear, single handwritten digit.")
-
-    except Exception as e:
-        logger.error(f"Error processing image: {e}")
-        st.error("An error occurred while processing the image. Please try again with a different image.")
-else:
-    st.info("Please upload an image to proceed.")
-
-# Instructions for users
-st.markdown("""
-### Instructions
-1. Upload an image containing a single handwritten digit (0-9).
-2. Ensure the digit is clear, centered, and on a plain background for best results.
-3. The model expects white digits on a black background, similar to MNIST dataset images.
-""")
+demo.launch()