commit

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+from huggingface_hub import hf_hub_download
+import tensorflow as tf
+
+# Load model
+model = tf.keras.models.load_model(
+    hf_hub_download("nharshavardhana/quickdraw_classifier", "quickdraw_classifier.keras")
+)
+
+# Class names (replace with your 50 classes)
+class_names = ['anvil','banana','bowtie','butterfly','cake','carrot','cat','clock','mushroom','cup','door', 'dog','eye','fish','hexagon','moon','ice cream','pizza','umbrella','circle','star','triangle','apple', 'car', 'house', 'tree', 'cloud', 'face', 'flower', 'bird']  # Add all 50 labels
+
+def predict_uploaded_image(img):
+    # Preprocess image
+    img = img.astype("float32") / 255.0
+    img = 1.0 - img  # Invert colors (if needed)
+    img = cv2.resize(img, (28, 28))
+    img = np.expand_dims(img, axis=(0, -1))
+    
+    # Predict
+    preds = model.predict(img)[0]
+    top5 = np.argsort(preds)[::-1][:5]
+    return {class_names[i]: float(preds[i]) for i in top5}
+
+# Create a detailed UI with Blocks
+with gr.Blocks(title="DoodleSense") as demo:
+    gr.Markdown("# 🎨 DoodleSense")
+    gr.Markdown("""
+    **Draw a sketch in paint application with brush(black) of 30 px(pixels) against white background and upload the saved image** to see the top 5 predictions!  
+    This model is trained on the [QuickDraw Dataset](https://quickdraw.withgoogle.com/data) for 30 classes.
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(
+                image_mode="L",
+            )
+            gr.Examples(
+                examples=["examples/butterfly.png", "examples/car.png"],  # Add your example images
+                inputs=input_image,
+                label="Try these examples:"
+            )
+        with gr.Column():
+            output_label = gr.Label(num_top_classes=5, label="Top 5 Predictions")
+    
+    gr.Markdown("""
+    ## 📖 About This Project
+    - **Model**: Trained using TensorFlow/Keras on 30 QuickDraw classes.
+    - **Input**: 28x28 grayscale sketches (black strokes on white background).
+    - **Training Data**: 50,000 samples per class from the QuickDraw dataset.
+    """)
+
+    input_image.change(predict_uploaded_image, inputs=input_image, outputs=output_label)
+
+demo.launch(share=True)

requirements.txt

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+gradio>=4.0.0
+tensorflow
+opencv-python-headless
+huggingface_hub
+numpy