Update README.md

README.md

@@ -1,4 +1,62 @@
 ---
 license: apache-2.0
 pipeline_tag: image-classification
----
+---
+# Devanagari Character Recognition
+
+```python
+# Example Code: You can test our model in Google Colab or Any where you want
+import requests
+from tensorflow.keras.models import load_model
+
+# Download the model from Hugging Face
+url = "https://huggingface.co/krishnamishra8848/Devanagari_Character_Recognition/resolve/main/saved_model.keras"
+model_path = "saved_model.keras"
+
+response = requests.get(url)
+with open(model_path, "wb") as f:
+    f.write(response.content)
+
+# Load the model
+model = load_model(model_path)
+
+# Nepali characters 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"
+]
+
+# File upload
+uploaded = files.upload()
+
+# Process the uploaded image
+for filename in uploaded.keys():
+    # Load the image
+    img = Image.open(filename)
+
+    # Convert the image to grayscale if necessary
+    img = np.array(img)
+    if len(img.shape) == 3:  # If the image is RGB
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+
+    # Resize to 32x32
+    img_resized = cv2.resize(img, (32, 32))
+
+    # Normalize the pixel values
+    img_normalized = img_resized.astype("float32") / 255.0
+
+    # Reshape to match the model's input shape
+    img_input = img_normalized.reshape(1, 32, 32, 1)
+
+    # Make a prediction
+    prediction = model.predict(img_input)
+    predicted_class_index = np.argmax(prediction)
+
+    # Get the predicted Nepali character
+    predicted_character = label_mapping[predicted_class_index]
+    print(f"Predicted Character: {predicted_character}")
+
+