Update app.py

app.py

@@ -1,7 +1,13 @@
-import gradio as gr 
-from sklearn.neural_network import MLPClassifier 
-import torchvision.datasets as datasets 
-import seaborn as sns 
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import JSONResponse
+from sklearn.neural_network import MLPClassifier
+import torchvision.datasets as datasets
+import numpy as np
+from PIL import Image
+from io import BytesIO
+import seaborn as sns
+
+app = FastAPI()
 
 # Dark mode seaborn 
 sns.set_style("darkgrid")
@@ -10,7 +16,7 @@ sns.set_style("darkgrid")
 mnist_trainset = datasets.MNIST(root='./data', train=True, download=True, transform=None)
 mnist_testset = datasets.MNIST(root='./data', train=False, download=True, transform=None)
 
-X_train = mnist_trainset.data.numpy() 
+X_train = mnist_trainset.data.numpy()
 X_test = mnist_testset.data.numpy()
 y_train = mnist_trainset.targets.numpy()
 y_test = mnist_testset.targets.numpy()
@@ -23,15 +29,14 @@ X_test = X_test.reshape(10000, 784) / 255.0
 mlp = MLPClassifier(hidden_layer_sizes=(32, 32))
 mlp.fit(X_train, y_train)
 
-# Print the accuracies 
-print("Training Accuracy: ", mlp.score(X_train, y_train))
-print("Testing Accuracy: ", mlp.score(X_test, y_test))
-
-# Define prediction function
-def predict(img):
-    img = img.reshape(1, 784) / 255.0
-    prediction = mlp.predict(img)[0]
-    return int(prediction)
-
-# Launch Gradio interface
-gr.Interface(fn=predict, inputs="sketchpad", outputs="label").launch()
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    try:
+        contents = await file.read()
+        image = Image.open(BytesIO(contents)).convert("L").resize((28, 28))
+        img_array = np.array(image)
+        img_array = img_array.flatten() / 255.0
+        prediction = mlp.predict(img_array.reshape(1, -1))[0]
+        return JSONResponse(content={"prediction": int(prediction)})
+    except Exception as e:
+        return JSONResponse(content={"error": str(e)})