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app.py

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+import pickle
+import gradio as gr
+import numpy as np
+from PIL import Image
+
+
+with open("digit_model.pkl", "rb") as f:
+    pipeline = pickle.load(f)
+
+def predict_manual(*features):
+    features = np.array(features, dtype=float).reshape(1, -1)
+    prediction = pipeline.predict(features)
+    return int(prediction[0])
+
+def predict_image(img):
+
+    display_img = img.resize((200, 200))
+    
+
+    img_small = img.convert("L").resize((8, 8), resample=Image.Resampling.LANCZOS)
+    img_array = np.array(img_small, dtype=float)
+
+    img_array = (16 - (img_array / 255.0 * 16)).flatten().reshape(1, -1)
+    
+    prediction = pipeline.predict(img_array)
+    return display_img, int(prediction[0])
+
+manual_inputs = [gr.Number(label=f"Pixel {i}", value=0) for i in range(64)]
+
+with gr.Blocks(title="Digit Recognition App") as demo:
+    gr.Markdown("## Digit Recognition using PCA + KNN Pipeline")
+    
+
+    with gr.Tab("Manual Input"):
+        gr.Markdown("Enter the 64 pixel values manually (0–16 scale like sklearn digits):")
+        for inp in manual_inputs:
+            inp.render()
+        manual_output = gr.Label(label="Predicted Digit")
+        manual_button = gr.Button("Predict Digit")
+        manual_button.click(fn=predict_manual, inputs=manual_inputs, outputs=manual_output)
+ 
+    with gr.Tab("Upload Image"):
+        gr.Markdown("Upload an image of a digit (grayscale or color):")
+        with gr.Row():
+            image_input = gr.Image(type="pil", label="Upload Digit Image")
+            with gr.Column():
+                image_output = gr.Image(label="Resized Image for Display")
+                digit_output = gr.Label(label="Predicted Digit")
+        
+        image_button = gr.Button("Predict Digit")
+        image_button.click(fn=predict_image, inputs=image_input, outputs=[image_output, digit_output])
+
+demo.launch()

digit_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:800d4f07bcca675c7ff3e4d3459f3f548d580d62192ff7264dad6104f98074f5
+size 1053218

requirements.txt

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+pandas
+numpy
+matplotlib
+seaborn
+scikit-learn