Update app.py

app.py

@@ -1,26 +1,25 @@
-from fastapi import FastAPI, File, UploadFile
-from fastapi.responses import JSONResponse
 import pickle
 import numpy as np
 from PIL import Image
+import gradio as gr
 
-app = FastAPI()
+# load your pickled RandomForest
 model = pickle.load(open("mnist_model.pkl", "rb"))
 
-def preprocess_image(file_bytes) -> np.ndarray:
-    # 1) Load into PIL, convert to grayscale 'L'
-    img = Image.open(file_bytes).convert("L")
-    # 2) Resize to 28×28 (use ANTIALIAS for quality)
-    img = img.resize((28,28), Image.ANTIALIAS)
-    # 3) Convert to numpy array (uint8), flatten to length-784
-    arr = np.array(img).astype("uint8").reshape(1, -1)
-    # 4) Optionally invert colors if your MNIST is white-on-black:
-    #    arr = 255 - arr
-    return arr
-
-@app.post("/predict-image/")
-async def predict_image(file: UploadFile = File(...)):
-    # read the incoming UploadFile into BytesIO
-    arr = preprocess_image(file.file)
+def classify_digit(img: Image.Image) -> str:
+    # convert to 28×28 grayscale
+    gray = img.convert("L").resize((28, 28))
+    arr = np.array(gray).reshape(1, -1)
     pred = model.predict(arr)[0]
-    return JSONResponse({"prediction": int(pred)})
+    return f"Predicted digit: {pred}"
+
+demo = gr.Interface(
+    fn=classify_digit,
+    inputs=gr.inputs.Image(type="pil", label="Upload a 28×28 digit"),
+    outputs=gr.outputs.Textbox(label="Prediction"),
+    title="Digit Classifier",
+    description="Upload a handwritten MNIST digit and get a prediction!"
+)
+
+if __name__ == "__main__":
+    demo.launch()