Update app.py

app.py

@@ -4,16 +4,19 @@ from tensorflow.keras.models import load_model
 from PIL import Image
 import requests
 
-# Load the model from Hugging Face
+# Cache the model with st.cache_resource
 @st.cache_resource
 def load_model_from_hf():
+    # Download the model from Hugging Face
     url = "https://huggingface.co/krishnamishra8848/Devanagari_Character_Recognition/resolve/main/saved_model.keras"
     response = requests.get(url)
     with open("saved_model.keras", "wb") as f:
         f.write(response.content)
+    # Load the model
     model = load_model("saved_model.keras")
     return model
 
+# Load the model
 model = load_model_from_hf()
 
 # Nepali characters mapping
@@ -25,24 +28,28 @@ label_mapping = [
     "४", "५", "६", "७", "८", "९"
 ]
 
-# Streamlit app interface
+# Streamlit App
 st.title("Devanagari Character Recognition")
 st.write("Upload an image of a Devanagari character or digit, and the model will predict it.")
 
-# File uploader
-uploaded_file = st.file_uploader("Choose a file", type=["jpg", "png", "jpeg"])
+# File uploader for user to upload images
+uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
 
 if uploaded_file is not None:
-    # Load and preprocess the image
-    img = Image.open(uploaded_file).convert("L")  # Convert to grayscale
-    img_resized = img.resize((32, 32))  # Resize to 32x32
-    img_array = np.array(img_resized).astype("float32") / 255.0  # Normalize
-    img_input = img_array.reshape(1, 32, 32, 1)  # Reshape for the model
-
-    # Make prediction
-    prediction = model.predict(img_input)
-    predicted_class_index = np.argmax(prediction)
-    predicted_character = label_mapping[predicted_class_index]
-
-    # Display result
-    st.success(f"Predicted Character: {predicted_character}")
+    try:
+        # Preprocess the image
+        img = Image.open(uploaded_file).convert("L")  # Convert to grayscale
+        img_resized = img.resize((32, 32))  # Resize to 32x32
+        img_array = np.array(img_resized).astype("float32") / 255.0  # Normalize pixel values
+        img_input = img_array.reshape(1, 32, 32, 1)  # Reshape for the model
+
+        # Make prediction
+        prediction = model.predict(img_input)
+        predicted_class_index = np.argmax(prediction)
+        predicted_character = label_mapping[predicted_class_index]
+
+        # Display the predicted character
+        st.success(f"Predicted Character: {predicted_character}")
+
+    except Exception as e:
+        st.error(f"An error occurred: {e}")