Create app.py

app.py

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+import streamlit as st
+from huggingface_hub import from_pretrained_keras
+from PIL import Image
+import numpy as np
+import tensorflow as tf
+
+@st.cache_resource
+def load_model():
+    model = from_pretrained_keras("Jainam117/Sign_Langauge_digit_classification")
+    return model
+
+
+def preprocess_image(image):
+   
+    image = image.convert("L") 
+    image = image.resize((224, 224))  # Resize to 224x224, which the model expects
+    image_array = np.array(image) 
+    image_array = image_array / 255.0  
+    image_array = np.expand_dims(image_array, axis=-1) 
+    image_array = np.expand_dims(image_array, axis=0) 
+    return image_array
+
+
+def classify_image(model, image_array):
+    predictions = model.predict(image_array)
+    predicted_label = np.argmax(predictions, axis=1)[0]
+    return predicted_label
+
+st.title("Sign Language Digit Classification")
+st.write("Upload an image of a hand showing a digit, and the model will classify the digit.")
+
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+  
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    model = load_model()
+
+    preprocessed_image = preprocess_image(image)
+
+    predicted_digit = classify_image(model, preprocessed_image)
+
+    st.write(f"Predicted Digit: {predicted_digit}")