Update app.py

app.py

@@ -1,3 +1,13 @@
+# ----------------------------
+# FIX FOR HUGGING FACE TIMEOUT
+# ----------------------------
+import os
+os.environ["STREAMLIT_BROWSER_GATHER_USAGE_STATS"] = "false"
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+
+# ----------------------------
+# IMPORTS
+# ----------------------------
 import streamlit as st
 import tensorflow as tf
 from tensorflow import keras
@@ -8,15 +18,25 @@ from PIL import Image
 # ----------------------------
 # PAGE CONFIG
 # ----------------------------
-st.set_page_config(page_title="Captcha OCR", layout="centered")
-st.title("🔐 Captcha OCR using CRNN + CTC")
-st.write("Upload a captcha image and get prediction")
+st.set_page_config(
+    page_title="Captcha OCR",
+    page_icon="🔐",
+    layout="centered"
+)
+
+st.title("🔐 Captcha OCR")
+st.markdown("CRNN + CTC Model Deployment")
 
 # ----------------------------
 # LOAD CHARACTERS
 # ----------------------------
-with open("characters.txt", "r") as f:
-    characters = list(f.read().strip())
+@st.cache_resource
+def load_characters():
+    with open("characters.txt", "r") as f:
+        characters = list(f.read().strip())
+    return characters
+
+characters = load_characters()
 
 charToNum = layers.StringLookup(vocabulary=characters, mask_token=None)
 numToChar = layers.StringLookup(
@@ -26,25 +46,31 @@ numToChar = layers.StringLookup(
 )
 
 # ----------------------------
-# LOAD MODEL
+# LOAD MODEL (LAZY + SAFE)
 # ----------------------------
 @st.cache_resource
 def load_model():
-    model = keras.models.load_model("ocr_model.keras")
+    model = keras.models.load_model(
+        "ocr_model.keras",
+        compile=False  # IMPORTANT for memory reduction
+    )
     return model
 
-model = load_model()
+# Lazy loading (prevents reload crash)
+if "model" not in st.session_state:
+    st.session_state.model = load_model()
+
+model = st.session_state.model
 
 # ----------------------------
 # PREPROCESS FUNCTION
 # ----------------------------
 def preprocess_image(image):
     image = image.convert("L")  # grayscale
-    image = image.resize((200, 50))  # SAME as training
-    image = np.array(image)
-    image = image.astype("float32") / 255.0
+    image = image.resize((200, 50))  # same as training
+    image = np.array(image).astype("float32") / 255.0
     image = np.expand_dims(image, axis=-1)
-    image = np.transpose(image, (1, 0, 2))  # IMPORTANT
+    image = np.transpose(image, (1, 0, 2))  # IMPORTANT (match training)
     image = np.expand_dims(image, axis=0)
     return image
 
@@ -72,15 +98,20 @@ def decode_prediction(pred):
 # ----------------------------
 # FILE UPLOADER
 # ----------------------------
-uploaded_file = st.file_uploader("Upload Captcha Image", type=["png", "jpg", "jpeg"])
+uploaded_file = st.file_uploader(
+    "Upload Captcha Image",
+    type=["png", "jpg", "jpeg"]
+)
 
 if uploaded_file is not None:
     image = Image.open(uploaded_file)
+
     st.image(image, caption="Uploaded Image", use_column_width=True)
 
     processed = preprocess_image(image)
 
-    prediction = model.predict(processed)
-    text = decode_prediction(prediction)
+    with st.spinner("Predicting..."):
+        prediction = model.predict(processed)
+        text = decode_prediction(prediction)
 
     st.success(f"🎯 Prediction: {text}")