Create app.py

app.py

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+import streamlit as st
+import numpy as np
+import cv2
+from tensorflow.keras.models import load_model
+from PIL import Image
+import requests
+
+# Load the model from Hugging Face
+@st.cache(allow_output_mutation=True)
+def load_model_from_hf():
+    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)
+    model = load_model("saved_model.keras")
+    return model
+
+model = load_model_from_hf()
+
+# Label mapping
+label_mapping = [
+    "क", "ख", "ग", "घ", "ङ", "च", "छ", "ज", "झ", "ञ",
+    "ट", "ठ", "ड", "ढ", "ण", "त", "थ", "द", "ध", "न",
+    "प", "फ", "ब", "भ", "म", "य", "र", "ल", "व", "श",
+    "ष", "स", "ह", "क्ष", "त्र", "ज्ञ", "०", "१", "२", "३",
+    "४", "५", "६", "७", "८", "९"
+]
+
+# Streamlit app interface
+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"])
+
+if uploaded_file is not None:
+    # Load and preprocess the image
+    img = Image.open(uploaded_file)
+    img = img.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
+
+    # Display uploaded image
+    st.image(img, caption="Uploaded Image", use_column_width=True)
+
+    # 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}")